2020.07.21.txt

==========New Papers==========
1, TITLE:       Jupyter Notebooks on GitHub: Characteristics and Code Clones
http://arxiv.org/abs/2007.10146
AUTHORS:        Malin Källén ; Ulf Sigvardsson ; Tobias Wrigstad
HIGHLIGHT:      In this paper we present the first large-scale study of code cloning in Jupyter notebooks.

2, TITLE:       Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining
http://arxiv.org/abs/2007.09163
AUTHORS:        Hao-Hsiang Yang ; Chao-Han Huck Yang ; Yu-Chiang Frank Wang
COMMENTS:       Accepted to IEEE ICIP 2020
HIGHLIGHT:      In this paper, we propose the new convolutional neural network (CNN) called the wavelet channel attention module with a fusion network.

3, TITLE:       Improving Object Detection with Selective Self-supervised Self-training
http://arxiv.org/abs/2007.09162
AUTHORS:        Yandong Li ; Di Huang ; Danfeng Qin ; Liqiang Wang ; Boqing Gong
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      To tackle this challenge, we propose a selective net to rectify the supervision signals in Web images.

4, TITLE:       A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
http://arxiv.org/abs/2007.10007
AUTHORS:        Munan Ning ; Cheng Bian ; Donghuan Lu ; Hong-Yu Zhou ; Shuang Yu ; Chenglang Yuan ; Yang Guo ; Yaohua Wang ; Kai Ma ; Yefeng Zheng
COMMENTS:       MICCAI 2020
HIGHLIGHT:      In this paper, we propose a novel framework to segment the target tissues accurately for the AS-OCT images, by using the combination of weakly-annotated images (majority) and fully-annotated images (minority).

5, TITLE:       Deep Hough-Transform Line Priors
http://arxiv.org/abs/2007.09493
AUTHORS:        Yancong Lin ; Silvia L. Pintea ; Jan C. van Gemert
COMMENTS:       ECCV 2020, code online: https://github.com/yanconglin/Deep-Hough-Transform-Line-Priors
HIGHLIGHT:      Here, we reduce the dependency on labeled data by building on the classic knowledge-based priors while using deep networks to learn features.

6, TITLE:       On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods
http://arxiv.org/abs/2007.10000
AUTHORS:        Kristijan Bartol ; David Bojanić ; Tomislav Pribanić ; Tomislav Petković ; Yago Diez Donoso ; Joaquim Salvi Mas
HIGHLIGHT:      The purpose of this study is to give a performance comparison between several classic hand-crafted and deep key-point detector and descriptor methods.

7, TITLE:       A novel approach to sentiment analysis in Persian using discourse and external semantic information
http://arxiv.org/abs/2007.09495
AUTHORS:        Rahim Dehkharghani ; Hojjat Emami
COMMENTS:       15 pages
HIGHLIGHT:      The proposed approach in this paper is two-fold: The first one is based on classifier combination, and the second one is based on deep neural networks which benefits from word embedding vectors.

8, TITLE:       Deep Image Clustering with Category-Style Representation
http://arxiv.org/abs/2007.10004
AUTHORS:        Junjie Zhao ; Donghuan Lu ; Kai Ma ; Yu Zhang ; Yefeng Zheng
COMMENTS:       Accepted at ECCV 2020. Project address: https://github.com/sKamiJ/DCCS
HIGHLIGHT:      In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.

9, TITLE:       Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
http://arxiv.org/abs/2007.10315
AUTHORS:        Yang Zou ; Xiaodong Yang ; Zhiding Yu ; B. V. K. Vijaya Kumar ; Jan Kautz
COMMENTS:       ECCV 2020 (Oral)
HIGHLIGHT:      In this paper, we seek to improve adaptation by purifying the representation space to be adapted.

10, TITLE:      MCUNet: Tiny Deep Learning on IoT Devices
http://arxiv.org/abs/2007.10319
AUTHORS:        Ji Lin ; Wei-Ming Chen ; Yujun Lin ; John Cohn ; Chuang Gan ; Song Han
COMMENTS:       Demo video available here: https://youtu.be/YvioBgtec4U
HIGHLIGHT:      We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers.

11, TITLE:      Deep Learning Based Brain Tumor Segmentation: A Survey
http://arxiv.org/abs/2007.09479
AUTHORS:        Zhihua Liu ; Long Chen ; Lei Tong ; Feixiang Zhou ; Zheheng Jiang ; Qianni Zhang ; Caifeng Shan ; Yinhai Wang ; Xiangrong Zhang ; Ling Li ; Huiyu Zhou
HIGHLIGHT:      Considering state-of-the-art technologies and their performance, the purpose of this paper is to provide a comprehensive survey of recently developed deep learning based brain tumor segmentation techniques.

12, TITLE:      Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection
http://arxiv.org/abs/2007.09478
AUTHORS:        Hangwei Zhuang ; Nabil Ettehadi
COMMENTS:       6 pages, 9 figures
HIGHLIGHT:      In this paper, we propose two distinct solutions to the problem of Diabetic Retinopathy (DR) classification.

13, TITLE:      Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition
http://arxiv.org/abs/2007.09344
AUTHORS:        Wen Ji ; Kelei He ; Jing Huo ; Zheng Gu ; Yang Gao
COMMENTS:       This paper has been accepted by ECCV 2020
HIGHLIGHT:      To facility the research in attribute learning of caricatures, we propose a caricature attribute dataset, namely WebCariA.

14, TITLE:      Combinatorial and computational investigations of Neighbor-Joining bias
http://arxiv.org/abs/2007.09345
AUTHORS:        Ruth Davidson ; Abraham Martin del Campo
COMMENTS:       17 pages, 11 figures
HIGHLIGHT:      We resolve this confusion by defining agglomeration orders on trees, leading to a bijection between distinct regions of the output space and weighted Motzkin paths.

15, TITLE:      CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus
http://arxiv.org/abs/2007.10310
AUTHORS:        Changhan Wang ; Anne Wu ; Juan Pino
HIGHLIGHT:      CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus

16, TITLE:      Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform
http://arxiv.org/abs/2007.09471
AUTHORS:        Alberto Santamaria-Pang ; Anup Sood ; Dan Meyer ; Aritra Chowdhury ; Fiona Ginty
COMMENTS:       2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
HIGHLIGHT:      We present a method for automatic cell classification in tissue samples using an automated training set from multiplexed immunofluorescence images.

17, TITLE:      Social Adaptive Module for Weakly-supervised Group Activity Recognition
http://arxiv.org/abs/2007.09470
AUTHORS:        Rui Yan ; Lingxi Xie ; Jinhui Tang ; Xiangbo Shu ; Qi Tian
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data.

18, TITLE:      Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting
http://arxiv.org/abs/2007.09482
AUTHORS:        Minghui Liao ; Guan Pang ; Jing Huang ; Tal Hassner ; Xiang Bai
COMMENTS:       Accepted by ECCV 2020
HIGHLIGHT:      To tackle these problems, we propose Mask TextSpotter v3, an end-to-end trainable scene text spotter that adopts a Segmentation Proposal Network (SPN) instead of an RPN.

19, TITLE:      Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit
http://arxiv.org/abs/2007.09483
AUTHORS:        Emma Rocheteau ; Pietro Liò ; Stephanie Hyland
COMMENTS:       arXiv admin note: substantial text overlap with arXiv:2006.16109
HIGHLIGHT:      In this work, we propose a new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU critical care dataset.

20, TITLE:      Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization
http://arxiv.org/abs/2007.09316
AUTHORS:        Shujun Wang ; Lequan Yu ; Caizi Li ; Chi-Wing Fu ; Pheng-Ann Heng
COMMENTS:       Accepted at ECCV 2020. Code is available at https://github.com/EmmaW8/EISNet
HIGHLIGHT:      To this end, we present a new domain generalization framework that learns how to generalize across domains simultaneously from extrinsic relationship supervision and intrinsic self-supervision for images from multi-source domains.

21, TITLE:      Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
http://arxiv.org/abs/2007.09314
AUTHORS:        Mang Ye ; Jianbing Shen ; David J. Crandall ; Ling Shao ; Jiebo Luo
COMMENTS:       Accepted by ECCV20
HIGHLIGHT:      In this paper, we propose a novel dynamic dual-attentive aggregation (DDAG) learning method by mining both intra-modality part-level and cross-modality graph-level contextual cues for VI-ReID.

22, TITLE:      Robust Image Classification Using A Low-Pass Activation Function and DCT Augmentation
http://arxiv.org/abs/2007.09453
AUTHORS:        Md Tahmid Hossain ; Shyh Wei Teng ; Ferdous Sohel ; Guojun Lu
HIGHLIGHT:      In this work, we analyse common corruptions from a frequency perspective, i.e., High Frequency corruptions or HFc (e.g., noise) and Low Frequency corruptions or LFc (e.g., blur).

23, TITLE:      ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation
http://arxiv.org/abs/2007.09455
AUTHORS:        Tianchen Wang ; Xiaowei Xu ; Jinjun Xiong ; Qianjun Jia ; Haiyun Yuan ; Meiping Huang ; Jian Zhuang ; Yiyu Shi
COMMENTS:       MICCAI2020, 12 pages, 3 figures
HIGHLIGHT:      In this work, inspired by a new interpretation of Independent Component Analysis (ICA) for learning, we propose a novel ICA-UNet for real-time 3D cardiac cine MRI segmentation.

24, TITLE:      Face Super-Resolution Guided by 3D Facial Priors
http://arxiv.org/abs/2007.09454
AUTHORS:        Xiaobin Hu ; Wenqi Ren ; John LaMaster ; Xiaochun Cao ; Xiaoming Li ; Zechao Li ; Bjoern Menze ; Wei Liu
COMMENTS:       Accepted as a spotlight paper, European Conference on Computer Vision 2020 (ECCV)
HIGHLIGHT:      In this paper, we propose a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures.

25, TITLE:      On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning
http://arxiv.org/abs/2007.09456
AUTHORS:        Guillem Ramírez ; Rumen Dangovski ; Preslav Nakov ; Marin Soljačić
HIGHLIGHT:      Here, we analyze popular methods for UCL and we find that often their objectives are, intrinsically, versions of the Wasserstein-Procrustes problem.

26, TITLE:      An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining
http://arxiv.org/abs/2007.09310
AUTHORS:        Artem Polyvyanyy ; Alistair Moffat ; Luciano García-Bañuelos
COMMENTS:       8 pages. Submitted for review on July 15, 2020
HIGHLIGHT:      Here we present an entropic relevance measure for stochastic conformance checking, computed as the average number of bits required to compress each of the log's traces, based on the structure and information about relative likelihoods provided by the model.

27, TITLE:      E$^2$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
http://arxiv.org/abs/2007.09791
AUTHORS:        Youbao Tang ; Yuxing Tang ; Yingying Zhu ; Jing Xiao ; Ronald M. Summers
HIGHLIGHT:      In this work, we propose a two-stage framework for 2D liver and tumor segmentation.

28, TITLE:      Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition
http://arxiv.org/abs/2007.09790
AUTHORS:        Ariel Ruiz-Garcia ; Vasile Palade ; Mark Elshaw ; Mariette Awad
COMMENTS:       Accepted at IJCNN 2020
HIGHLIGHT:      In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions, with up to plus or minus 60 degrees, to an illumination invariant facial representation of 0 degrees.

29, TITLE:      Object-Centric Multi-View Aggregation
http://arxiv.org/abs/2007.10300
AUTHORS:        Shubham Tulsiani ; Or Litany ; Charles Qi ; Leonidas Guibas
HIGHLIGHT:      We present an approach for aggregating a sparse set of views of an object in order to compute a semi-implicit 3D representation in the form of a volumetric feature grid.

30, TITLE:      Feature Pyramid Transformer
http://arxiv.org/abs/2007.09451
AUTHORS:        Dong Zhang ; Hanwang Zhang ; Jinhui Tang ; Meng Wang ; Xiansheng Hua ; Qianru Sun
COMMENTS:       Published at the European Conference on Computer Vision, 2020
HIGHLIGHT:      To this end, we propose a fully active feature interaction across both space and scales, called Feature Pyramid Transformer (FPT).

31, TITLE:      PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation
http://arxiv.org/abs/2007.09465
AUTHORS:        Jue Jiang ; Yu Chi Hu ; Neelam Tyagi ; Andreas Rimner ; Nancy Lee ; Joseph O. Deasy ; Sean Berry ; Harini Veeraraghavan
COMMENTS:       This paper has been accepted by IEEE Transactions on Medical Imaging
HIGHLIGHT:      We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images.

32, TITLE:      ESCELL: Emergent Symbolic Cellular Language
http://arxiv.org/abs/2007.09469
AUTHORS:        Aritra Chowdhury ; James R. Kubricht ; Anup Sood ; Peter Tu ; Alberto Santamaria-Pang
COMMENTS:       IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2020)
HIGHLIGHT:      We present ESCELL, a method for developing an emergent symbolic language of communication between multiple agents reasoning about cells.

33, TITLE:      Analysis of Bayesian Networks via Prob-Solvable Loops
http://arxiv.org/abs/2007.09450
AUTHORS:        Ezio Bartocci ; Laura Kovács ; Miroslav Stankovič
HIGHLIGHT:      In this paper we extend Prob-solvable loops with new features essential for encoding Bayesian networks (BNs).

34, TITLE:      Unsupervised Shape Normality Metric for Severity Quantification
http://arxiv.org/abs/2007.09307
AUTHORS:        Wenzheng Tao ; Riddhish Bhalodia ; Erin Anstadt ; Ladislav Kavan ; Ross T. Whitaker ; Jesse A. Goldstein
HIGHLIGHT:      This work describes an unsupervised method to objectively quantify the abnormality of general anatomical shapes.

35, TITLE:      A Bag of Visual Words Model for Medical Image Retrieval
http://arxiv.org/abs/2007.09464
AUTHORS:        Sowmya Kamath S ; Karthik K
COMMENTS:       In the proceedings of the 7th International Engineering Symposium (IES 2018), Kumamoto University, Kumamoto, Japan, Mar 7-9, 2018
HIGHLIGHT:      In this paper, we present a MedIR approach based on the BoVW model for content-based medical image retrieval.

36, TITLE:      PaSh: Light-touch Data-Parallel Shell Processing
http://arxiv.org/abs/2007.09436
AUTHORS:        Nikos Vasilakis ; Konstantinos Kallas ; Konstantinos Mamouras ; Achilleas Benetopoulos ; Lazar Cvetkovich
COMMENTS:       15 pages, 9 figures
HIGHLIGHT:      This paper presents PaSh, a shell aimed at parallelizing POSIX shell scripts through a combination of program transformations and runtime primitives.

37, TITLE:      LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation
http://arxiv.org/abs/2007.09319
AUTHORS:        Tak-Wai Hui ; Chen Change Loy
COMMENTS:       Accepted to ECCV 2020. Trained models and code package are available at https://github.com/twhui/LiteFlowNet3
HIGHLIGHT:      In this paper, we introduce LiteFlowNet3, a deep network consisting of two specialized modules, to address the above challenges.

38, TITLE:      Monochromatic Triangles, Triangle Listing and APSP
http://arxiv.org/abs/2007.09318
AUTHORS:        Virginia Vassilevska Williams ; Yinzhan Xu
COMMENTS:       To appear in FOCS'20
HIGHLIGHT:      In this paper we find more relationships between these two problems and other basic problems.

39, TITLE:      Deep Representation Learning For Multimodal Brain Networks
http://arxiv.org/abs/2007.09777
AUTHORS:        Wen Zhang ; Liang Zhan ; Paul Thompson ; Yalin Wang
COMMENTS:       11 pages, 3 figures, MICCAI 2020
HIGHLIGHT:      To address these challenges, we propose a novel end-to-end deep graph representation learning (Deep Multimodal Brain Networks - DMBN) to fuse multimodal brain networks.

40, TITLE:      An Overview of Natural Language State Representation for Reinforcement Learning
http://arxiv.org/abs/2007.09774
AUTHORS:        Brielen Madureira ; David Schlangen
COMMENTS:       Accepted to the ICML 2020 Workshop on Language in Reinforcement Learning (LaReL). 4 pages
HIGHLIGHT:      This survey outlines the strategies used in the literature to build natural language state representations.

41, TITLE:      A Separation Logic to Verify Termination of Busy-Waiting for Abrupt Program Exit: Technical Report
http://arxiv.org/abs/2007.10215
AUTHORS:        Tobias Reinhard ; Amin Timany ; Bart Jacobs
COMMENTS:       22 pages, 14 figures, Technical report
HIGHLIGHT:      In this paper, we make a first step towards proving termination of such programs.

42, TITLE:      Bit-Slicing the Hilbert Space: Scaling Up Accurate Quantum Circuit Simulation to a New Level
http://arxiv.org/abs/2007.09304
AUTHORS:        Yuan-Hung Tsai ; Jie-Hong R. Jiang ; Chiao-Shan Jhang
COMMENTS:       9 pages, 2 figures
HIGHLIGHT:      In this paper, we enhance quantum circuit simulation in two dimensions: accuracy and scalability.

43, TITLE:      DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization
http://arxiv.org/abs/2007.09217
AUTHORS:        Juan Du ; Rui Wang ; Daniel Cremers
COMMENTS:       ECCV 2020, sportlight
HIGHLIGHT:      For relocalization in large-scale point clouds, we propose the first approach that unifies global place recognition and local 6DoF pose refinement.

44, TITLE:      Improving the HardNet Descriptor
http://arxiv.org/abs/2007.09699
AUTHORS:        Milan Pultar
COMMENTS:       The thesis was supervised by Dmytro Mishkin. Many pieces of advice came from Ji\v{r}\'i Matas
HIGHLIGHT:      In the thesis we consider the problem of local feature descriptor learning for wide baseline stereo focusing on the HardNet descriptor, which is close to state-of-the-art.

45, TITLE:      Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images
http://arxiv.org/abs/2007.09695
AUTHORS:        Yi Zhong
HIGHLIGHT:      This project utilizes several open-source or public datasets to present an open-source dataset of COVID-19 CXRs, named COVID-19-CXR-Dataset, and introduces a deep convolutional neural network model.

46, TITLE:      Class-wise Dynamic Graph Convolution for Semantic Segmentation
http://arxiv.org/abs/2007.09690
AUTHORS:        Hanzhe Hu ; Deyi Ji ; Weihao Gan ; Shuai Bai ; Wei Wu ; Junjie Yan
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      In order to avoid potential misleading contextual information aggregation in previous works, we propose a class-wise dynamic graph convolution (CDGC) module to adaptively propagate information.

47, TITLE:      Can we cover navigational perception needs of the visually impaired by panoptic segmentation?
http://arxiv.org/abs/2007.10202
AUTHORS:        Wei Mao ; Jiaming Zhang ; Kailun Yang ; Rainer Stiefelhagen
COMMENTS:       14 pages, 6 Figures, 2 tables
HIGHLIGHT:      Motivated by that, we propose to utilize panoptic segmentation as an approach to navigating visually impaired people by offering both things and stuff awareness in the proximity of the visually impaired.

48, TITLE:      People as Scene Probes
http://arxiv.org/abs/2007.09209
AUTHORS:        Yifan Wang ; Brian Curless ; Steve Seitz
COMMENTS:       ECCV 2020
HIGHLIGHT:      We demonstrate results (best viewed in supplementary video) on a range of scenes and compare to alternative methods for depth estimation and shadow compositing.

49, TITLE:      Learn distributed GAN with Temporary Discriminators
http://arxiv.org/abs/2007.09221
AUTHORS:        Hui Qu ; Yikai Zhang ; Qi Chang ; Zhennan Yan ; Chao Chen ; Dimitris Metaxas
COMMENTS:       Accepted by ECCV2020. Code: https://github.com/huiqu18/TDGAN-PyTorch
HIGHLIGHT:      In this work, we propose a method for training distributed GAN with sequential temporary discriminators.

50, TITLE:      Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
http://arxiv.org/abs/2007.09222
AUTHORS:        Haoran Wang ; Tong Shen ; Wei Zhang ; Lingyu Duan ; Tao Mei
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains.

51, TITLE:      One-Shot Learning for Language Modelling
http://arxiv.org/abs/2007.09679
AUTHORS:        Talip Ucar ; Adrian Gonzalez-Martin ; Matthew Lee ; Adrian Daniel Szwarc
HIGHLIGHT:      In this work we tackle the problem of oneshot learning for an NLP task by employing ideas from recent developments in machine learning: embeddings, attention mechanisms (softmax) and similarity measures (cosine, Euclidean, Poincare, and Minkowski).

52, TITLE:      Learning Error-Driven Curriculum for Crowd Counting
http://arxiv.org/abs/2007.09676
AUTHORS:        Wenxi Li ; Zhuoqun Cao ; Qian Wang ; Songjian Chen ; Rui Feng
COMMENTS:       This paper is accepted by ICPR2020
HIGHLIGHT:      In this paper, we propose a novel learning strategy for learning error-driven curriculum, which uses an additional network to supervise the training of the main network.

53, TITLE:      Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation
http://arxiv.org/abs/2007.09669
AUTHORS:        Jue Jiang ; Harini Veeraraghavan
COMMENTS:       This paper has been accepted by MICCAI2020
HIGHLIGHT:      Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation.

54, TITLE:      OBA: An Ontology-Based Framework for Creating REST APIs for Knowledge Graphs
http://arxiv.org/abs/2007.09206
AUTHORS:        Daniel Garijo ; Maximiliano Osorio
HIGHLIGHT:      In this paper we describe the Ontology-Based APIs framework (OBA), our approach to automatically create REST APIs from ontologies while following RESTful API best practices.

55, TITLE:      Neural Networks with Recurrent Generative Feedback
http://arxiv.org/abs/2007.09200
AUTHORS:        Yujia Huang ; James Gornet ; Sihui Dai ; Zhiding Yu ; Tan Nguyen ; Doris Y. Tsao ; Anima Anandkumar
HIGHLIGHT:      The proposed framework, termed Convolutional Neural Networks with Feedback (CNN-F), introduces a generative feedback with latent variables into existing CNN architectures, making consistent predictions via alternating MAP inference under a Bayesian framework.

56, TITLE:      ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget
http://arxiv.org/abs/2007.10106
AUTHORS:        Guillaume Coiffier ; Ghouthi Boukli Hacene ; Vincent Gripon
HIGHLIGHT:      In an effort to use every parameter of a network at its maximum, we propose a new convolutional neural network architecture, called ThriftyNet.

57, TITLE:      Monte Carlo Dropout Ensembles for Robust Illumination Estimation
http://arxiv.org/abs/2007.10114
AUTHORS:        Firas Laakom ; Jenni Raitoharju ; Alexandros Iosifidis ; Jarno Nikkanen ; Moncef Gabbouj
COMMENTS:       7 pages,6 figures
HIGHLIGHT:      In this paper, we address this limitation by proposing to aggregate different deep learning methods according to their output uncertainty.

58, TITLE:      Adaptive Video Highlight Detection by Learning from User History
http://arxiv.org/abs/2007.09598
AUTHORS:        Mrigank Rochan ; Mahesh Kumar Krishna Reddy ; Linwei Ye ; Yang Wang
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      In this paper, we propose a simple yet effective framework that learns to adapt highlight detection to a user by exploiting the user's history in the form of highlights that the user has previously created.

59, TITLE:      Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes
http://arxiv.org/abs/2007.09594
AUTHORS:        Lei Yang ; Wenxi Liu ; Zhiming Cui ; Nenglun Chen ; Wenping Wang
COMMENTS:       Accepted to ECCV2020
HIGHLIGHT:      We propose an unsupervised learning framework with the pretext task of finding dense correspondences between point cloud shapes from the same category based on the cycle-consistency formulation.

60, TITLE:      AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation
http://arxiv.org/abs/2007.09590
AUTHORS:        Weiting Huang ; Pengfei Ren ; Jingyu Wang ; Qi Qi ; Haifeng Sun
COMMENTS:       Accepted by AAAI-2020
HIGHLIGHT:      In this paper, we propose an adaptive weighting regression (AWR) method to leverage the advantages of both detection-based and regression-based methods.

61, TITLE:      Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering
http://arxiv.org/abs/2007.09592
AUTHORS:        Ruixue Tang ; Chao Ma ; Wei Emma Zhang ; Qi Wu ; Xiaokang Yang
COMMENTS:       To appear in ECCV 2020
HIGHLIGHT:      In this paper, instead of directly manipulating images and questions, we use generated adversarial examples for both images and questions as the augmented data.

62, TITLE:      Length-Controllable Image Captioning
http://arxiv.org/abs/2007.09580
AUTHORS:        Chaorui Deng ; Ning Ding ; Mingkui Tan ; Qi Wu
COMMENTS:       To be appeared in ECCV 2020
HIGHLIGHT:      In this paper, we propose to use a simple length level embedding to endow them with this ability.

63, TITLE:      PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
http://arxiv.org/abs/2007.09584
AUTHORS:        Zhiming Chen ; Kean Chen ; Weiyao Lin ; John See ; Hui Yu ; Yan Ke ; Cong Yang
HIGHLIGHT:      Existing OBB approaches are mostly built on horizontal bounding box detectors by introducing an additional angle dimension optimized by a distance loss.

64, TITLE:      Referring Expression Comprehension: A Survey of Methods and Datasets
http://arxiv.org/abs/2007.09554
AUTHORS:        Yanyuan Qiao ; Chaorui Deng ; Qi Wu
HIGHLIGHT:      We classify methods by their mechanism to encode the visual and textual modalities.

65, TITLE:      Resolution Switchable Networks for Runtime Efficient Image Recognition
http://arxiv.org/abs/2007.09558
AUTHORS:        Yikai Wang ; Fuchun Sun ; Duo Li ; Anbang Yao
COMMENTS:       Accepted by ECCV 2020. Code and models are available at https://github.com/yikaiw/RS-Nets
HIGHLIGHT:      We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference.

66, TITLE:      From Spatial Relations to Spatial Configurations
http://arxiv.org/abs/2007.09557
AUTHORS:        Soham Dan ; Parisa Kordjamshidi ; Julia Bonn ; Archna Bhatia ; Jon Cai ; Martha Palmer ; Dan Roth
HIGHLIGHT:      We integrate this language with the Abstract Meaning Representation(AMR) annotation schema and present a corpus annotated by this extended AMR.

67, TITLE:      Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities
http://arxiv.org/abs/2007.09569
AUTHORS:        Jincheng Mei ; Yangchen Pan ; Martha White ; Amir-massoud Farahmand ; Hengshuai Yao
COMMENTS:       The paper is under review
HIGHLIGHT:      In this work, we revisit prioritized ER and, in an ideal setting, show an equivalence to minimizing cubic loss, providing theoretical insight into why it improves upon uniform sampling.

68, TITLE:      Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning
http://arxiv.org/abs/2007.09549
AUTHORS:        Maunil R Vyas ; Hemanth Venkateswara ; Sethuraman Panchanathan
COMMENTS:       19 Pages, To be appear in ECCV 2020
HIGHLIGHT:      To address this concern, we propose the novel LsrGAN, a generative model that Leverages the Semantic Relationship between seen and unseen categories and explicitly performs knowledge transfer by incorporating a novel Semantic Regularized Loss (SR-Loss).

69, TITLE:      Kinematic 3D Object Detection in Monocular Video
http://arxiv.org/abs/2007.09548
AUTHORS:        Garrick Brazil ; Gerard Pons-Moll ; Xiaoming Liu ; Bernt Schiele
COMMENTS:       To appear in ECCV 2020
HIGHLIGHT:      In this work, we propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization.

70, TITLE:      Predicting risk of late age-related macular degeneration using deep learning
http://arxiv.org/abs/2007.09550
AUTHORS:        Yifan Peng ; Tiarnan D. Keenan ; Qingyu Chen ; Elvira Agrón ; Alexis Allot ; Wai T. Wong ; Emily Y. Chew ; Zhiyong Lu
COMMENTS:       Accepted by npj Digital Medicine
HIGHLIGHT:      Here, we demonstrate how deep learning and survival analysis can predict the probability of progression to late AMD using 3,298 participants (over 80,000 images) from the Age-Related Eye Disease Studies AREDS and AREDS2, the largest longitudinal clinical trials in AMD.

71, TITLE:      Progressive Multi-Scale Residual Network for Single Image Super-Resolution
http://arxiv.org/abs/2007.09552
AUTHORS:        Yuqing Liu ; Xinfeng Zhang ; Shanshe Wang ; Siwei Ma ; Wen Gao
HIGHLIGHT:      This paper proposes a progressive multi-scale residual network (PMRN) for single image super-resolution problem by sequentially exploiting features with restricted parameters.

72, TITLE:      Understanding Spatial Relations through Multiple Modalities
http://arxiv.org/abs/2007.09551
AUTHORS:        Soham Dan ; Hangfeng He ; Dan Roth
HIGHLIGHT:      In this paper, we introduce the task of inferring implicit and explicit spatial relations between two entities in an image.

73, TITLE:      DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection
http://arxiv.org/abs/2007.09431
AUTHORS:        Dongyun Lin ; Yiqun Li ; Shudong Xie ; Tin Lay Nwe ; Sheng Dong
HIGHLIGHT:      In this paper, we propose an AD method called dual deep reconstruction networks based image decomposition (DDR-ID).

74, TITLE:      Volumetric Transformer Networks
http://arxiv.org/abs/2007.09433
AUTHORS:        Seungryong Kim ; Sabine Süsstrunk ; Mathieu Salzmann
COMMENTS:       ECCV 2020
HIGHLIGHT:      To overcome this limitation, we introduce a learnable module, the volumetric transformer network (VTN), that predicts channel-wise warping fields so as to reconfigure intermediate CNN features spatially and channel-wisely.

75, TITLE:      Autonomy and Unmanned Vehicles Augmented Reactive Mission-Motion Planning Architecture for Autonomous Vehicles
http://arxiv.org/abs/2007.09563
AUTHORS:        Somaiyeh MahmoudZadeh ; David MW Powers ; Reza Bairam Zadeh
HIGHLIGHT:      In-deed the research monograph, the review chapters and the new approaches we have developed would be appropriate for use as a reference in upper years or postgraduate degrees for its coverage of literature and algorithms relating to Robot/Vehicle planning, tasking, routing, and trust.

76, TITLE:      Structure Mapping for Transferability of Causal Models
http://arxiv.org/abs/2007.09445
AUTHORS:        Purva Pruthi ; Javier González ; Xiaoyu Lu ; Madalina Fiterau
COMMENTS:       Presented at the Inductive Biases, Invariances and Generalization in Reinforcement Learning Workshop, ICML 2020
HIGHLIGHT:      We use this intuition to design a transfer-learning framework using object-oriented representations to learn the causal relationships between objects.

77, TITLE:      Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability
http://arxiv.org/abs/2007.09448
AUTHORS:        Alberto Santamaria-Pang ; James Kubricht ; Aritra Chowdhury ; Chitresh Bhushan ; Peter Tu
COMMENTS:       Accepted to Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020, 9 pages, 3 figures
HIGHLIGHT:      Inspired by how humans communicate complex ideas through language, we developed a generalized Symbolic Semantic ($\text{S}^2$) framework for interpretable segmentation.

78, TITLE:      Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
http://arxiv.org/abs/2007.09536
AUTHORS:        Yu Meng ; Yunyi Zhang ; Jiaxin Huang ; Yu Zhang ; Chao Zhang ; Jiawei Han
COMMENTS:       KDD 2020 Research Track. (Code: https://github.com/yumeng5/JoSH)
HIGHLIGHT:      To guide the hierarchical topic discovery process with minimal user supervision, we propose a new task, Hierarchical Topic Mining, which takes a category tree described by category names only, and aims to mine a set of representative terms for each category from a text corpus to help a user comprehend his/her interested topics.

79, TITLE:      Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation
http://arxiv.org/abs/2007.09438
AUTHORS:        Dongyun Lin ; Yanpeng Cao ; Wenbing Zhu ; Yiqun Li
HIGHLIGHT:      We present two effective regularization techniques via incorporating abundant defect-free images into the training of a UNet-like encoder-decoder defect segmentation network.

80, TITLE:      Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support
http://arxiv.org/abs/2007.09303
AUTHORS:        Xin Deng ; Ross Smith ; Genevieve Quintin
COMMENTS:       7 pages, 7 figures, 2 tables
HIGHLIGHT:      With the evolution of the cloud and customer centric culture, we inherently accumulate huge repositories of textual reviews, feedback, and support data.This has driven enterprises to seek and research engagement patterns, user network analysis, topic detections, etc.However, huge manual work is still necessary to mine data to be able to mine actionable outcomes.In this paper, we proposed and developed an innovative Semi-Supervised Learning approach by utilizing Deep Learning and Topic Modeling to have a better understanding of the user voice.This approach combines a BERT-based multiclassification algorithm through supervised learning combined with a novel Probabilistic and Semantic Hybrid Topic Inference (PSHTI) Model through unsupervised learning, aiming at automating the process of better identifying the main topics or areas as well as the sub-topics from the textual feedback and support.There are three major contributions and break-through:1.

81, TITLE:      An Open-World Simulated Environment for Developmental Robotics
http://arxiv.org/abs/2007.09300
AUTHORS:        SM Mazharul Islam ; Md Ashaduzzaman Rubel Mondol ; Aishwarya Pothula ; Deokgun Park
COMMENTS:       Presented at Workshop on Learning in Artificial Open Worlds held with ICML 2020
HIGHLIGHT:      In this paper, we introduce SEDRo, a Simulated Environment for Developmental Robotics which allows a learning agent to have similar experiences that a human infant goes through from the fetus stage up to 12 months.

82, TITLE:      ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
http://arxiv.org/abs/2007.09785
AUTHORS:        Rohun Tripathi ; Vasu Singla ; Mahyar Najibi ; Bharat Singh ; Abhishek Sharma ; Larry Davis
HIGHLIGHT:      In this article, we carefully profile Greedy-NMS iterations to find that a major chunk of computation is wasted in comparing proposals that are already far-away and have a small chance of suppressing each other.

83, TITLE:      Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier
http://arxiv.org/abs/2007.09898
AUTHORS:        Tz-Ying Wu ; Pedro Morgado ; Pei Wang ; Chih-Hui Ho ; Nuno Vasconcelos
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier

84, TITLE:      Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Images
http://arxiv.org/abs/2007.09892
AUTHORS:        Sai Bi ; Zexiang Xu ; Kalyan Sunkavalli ; Miloš Hašan ; Yannick Hold-Geoffroy ; David Kriegman ; Ravi Ramamoorthi
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      We present a deep learning approach to reconstruct scene appearance from unstructured images captured under collocated point lighting.

85, TITLE:      Improved Convergence Speed of Fully Symmetric Learning Rules for Principal Component Analysis
http://arxiv.org/abs/2007.09426
AUTHORS:        Ralf Möller
HIGHLIGHT:      Here we describe a modified objective function with an additional term which mitigates this convergence problem.

86, TITLE:      A Generic Visualization Approach for Convolutional Neural Networks
http://arxiv.org/abs/2007.09748
AUTHORS:        Ahmed Taha ; Xitong Yang ; Abhinav Shrivastava ; Larry Davis
COMMENTS:       ECCV'2020
HIGHLIGHT:      We formulate attention visualization as a constrained optimization problem.

87, TITLE:      On Algorithmic Estimation of Analytic Complexity for Polynomial Solutions to Hypergeometric Systems
http://arxiv.org/abs/2007.09407
AUTHORS:        Vitaly A. Krasikov
HIGHLIGHT:      The paper deals with the analytic complexity of solutions to bivariate holonomic hypergeometric systems of the Horn type.

88, TITLE:      Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation
http://arxiv.org/abs/2007.09746
AUTHORS:        Gabriel L. Oliveira ; Senthil Yogamani ; Wolfram Burgard ; Thomas Brox
HIGHLIGHT:      To address these limitations, we propose a new architecture based on a decoder which uses a set of shallow networks for capturing more information content. Additionally, we present a set of experiments augmenting semantic segmentation with optical flow information, showing that motion clues can boost pure image based semantic segmentation approaches.

89, TITLE:      Exploiting vulnerabilities of deep neural networks for privacy protection
http://arxiv.org/abs/2007.09766
AUTHORS:        Ricardo Sanchez-Matilla ; Chau Yi Li ; Ali Shahin Shamsabadi ; Riccardo Mazzon ; Andrea Cavallaro
HIGHLIGHT:      To address these limitations, we present an adversarial attack {that is} specifically designed to protect visual content against { unseen} classifiers and known defenses.

90, TITLE:      Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
http://arxiv.org/abs/2007.09763
AUTHORS:        Shasha Li ; Shitong Zhu ; Sudipta Paul ; Amit Roy-Chowdhury ; Chengyu Song ; Srikanth Krishnamurthy ; Ananthram Swami ; Kevin S Chan
COMMENTS:       The paper is accepted by ECCV 2020
HIGHLIGHT:      Our approach builds a set of auto-encoders, one for each object class, appropriately trained so as to output a discrepancy between the input and output if an added adversarial perturbation violates context consistency rules.

91, TITLE:      Graph Neural Network for Video-Query based Video Moment Retrieval
http://arxiv.org/abs/2007.09877
AUTHORS:        Yuan Zhou ; Mingfei Wang ; Ruolin Wang ; Shuwei Huo
HIGHLIGHT:      In this paper, we focus on Video Query based Video Moment Retrieval (VQ-VMR) task, which uses a query video clip as input to retrieve a semantic relative video clip in another untrimmed long video.

92, TITLE:      Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks
http://arxiv.org/abs/2007.09757
AUTHORS:        Diego de Vargas Feijo ; Viviane Pereira Moreira
HIGHLIGHT:      In this paper, our contribution is twofold.

93, TITLE:      Full Quaternion Representation of Color images: A Case Study on QSVD-based Color Image Compression
http://arxiv.org/abs/2007.09758
AUTHORS:        Alireza Parchami ; Mojtaba Mahdavi
COMMENTS:       15 pages, 16 figures, 1 table, submitted to Signal Processing journal
HIGHLIGHT:      In this paper, we propose an approach for representing color images with full quaternion numbers that enables us to process color images holistically without additional cost in time, space and computation.

94, TITLE:      Interpretable Foreground Object Search As Knowledge Distillation
http://arxiv.org/abs/2007.09867
AUTHORS:        Boren Li ; Po-Yu Zhuang ; Jian Gu ; Mingyang Li ; Ping Tan
COMMENTS:       This paper will appear at ECCV 2020
HIGHLIGHT:      This paper proposes a knowledge distillation method for foreground object search (FoS).

95, TITLE:      Geometry Constrained Weakly Supervised Object Localization
http://arxiv.org/abs/2007.09727
AUTHORS:        Weizeng Lu ; Xi Jia ; Weicheng Xie ; Linlin Shen ; Yicong Zhou ; Jinming Duan
COMMENTS:       This paper (ID 5424) is accepted to ECCV 2020
HIGHLIGHT:      We propose a geometry constrained network, termed GC-Net, for weakly supervised object localization (WSOL).

96, TITLE:      Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
http://arxiv.org/abs/2007.09654
AUTHORS:        Tong Wu ; Qingqiu Huang ; Ziwei Liu ; Yu Wang ; Dahua Lin
COMMENTS:       To appear in ECCV 2020 as a spotlight presentation. Code and models are available at: https://github.com/wutong16/DistributionBalancedLoss
HIGHLIGHT:      We present a new loss function called Distribution-Balanced Loss for the multi-label recognition problems that exhibit long-tailed class distributions.

97, TITLE:      Political Framing: US COVID19 Blame Game
http://arxiv.org/abs/2007.09655
AUTHORS:        Chereen Shurafa ; Kareem Darwish ; Wajdi Zaghouani
COMMENTS:       Social Informatics 2020 (SocInfo2020)
HIGHLIGHT:      In this paper, we show that the COVID19 pandemic rather than being viewed as a public health issue, political rhetoric surrounding it is mostly shaped through a blame frame (blame Trump, China, or conspiracies) and a support frame (support candidates) backing the agenda of Republican and Democratic users in the lead up to the 2020 presidential campaign.

98, TITLE:      Character Region Attention For Text Spotting
http://arxiv.org/abs/2007.09629
AUTHORS:        Youngmin Baek ; Seung Shin ; Jeonghun Baek ; Sungrae Park ; Junyeop Lee ; Daehyun Nam ; Hwalsuk Lee
COMMENTS:       17 pages, 9 figures, Accepted by ECCV 2020
HIGHLIGHT:      Based on the insight, we construct a tightly coupled single pipeline model.

99, TITLE:      Design and Analysis of a Multi-Agent E-Learning System Using Prometheus Design Tool
http://arxiv.org/abs/2007.09645
AUTHORS:        Kennedy E. Ehimwenma ; Sujatha krishnamoorthy
COMMENTS:       17 figures, 3 tables
HIGHLIGHT:      This paper presents the use of Prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents.

100, TITLE:     Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition
http://arxiv.org/abs/2007.10298
AUTHORS:        Darshan Gera ; S Balasubramanian
HIGHLIGHT:      Leveraging on the aforementioned observations, an end-to-end architecture for FER is proposed in this work that obtains both local and global attention per channel per spatial location through a novel spatio-channel attention net (SCAN), without seeking any information from the landmark detectors.

101, TITLE:     Survey on Deep Learning-based Kuzushiji Recognition
http://arxiv.org/abs/2007.09637
AUTHORS:        Kazuya Ueki ; Tomoka Kojima
HIGHLIGHT:      In recent years, competitions on Kuzushiji recognition have been held, and many researchers have proposed various recognition methods.

102, TITLE:     Modulation of viability signals for self-regulatory control
http://arxiv.org/abs/2007.09297
AUTHORS:        Alvaro Ovalle ; Simon M. Lucas
COMMENTS:       Accepted to the 1st International Workshop on Active Inference (non-final version)
HIGHLIGHT:      We revisit the role of instrumental value as a driver of adaptive behavior.

103, TITLE:     Slot Contrastive Networks: A Contrastive Approach for Representing Objects
http://arxiv.org/abs/2007.09294
AUTHORS:        Evan Racah ; Sarath Chandar
COMMENTS:       Presented at ICML 2020 Workshop: Object-Oriented Learning (OOL): Perception, Representation, and Reasoning
HIGHLIGHT:      Conversely, we present a new method that avoids losses in pixel space and over-reliance on the limited signal a static image provides.

104, TITLE:     Symbiotic Adversarial Learning for Attribute-based Person Search
http://arxiv.org/abs/2007.09609
AUTHORS:        Yu-Tong Cao ; Jingya Wang ; Dacheng Tao
COMMENTS:       17 pages, 5 figures. Accepted to ECCV2020
HIGHLIGHT:      In this paper, we present a symbiotic adversarial learning framework, called SAL.Two GANs sit at the base of the framework in a symbiotic learning scheme: one synthesizes features of unseen classes/categories, while the other optimizes the embedding and performs the cross-modal alignment on the common embedding space .

105, TITLE:     Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling
http://arxiv.org/abs/2007.10294
AUTHORS:        Omid Poursaeed ; Matthew Fisher ; Noam Aigerman ; Vladimir G. Kim
COMMENTS:       ECCV 2020
HIGHLIGHT:      We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i.e., embeddings of 2D domains into 3D; (ii) an implicit-function representation, i.e., a scalar function over the 3D volume, with its levels denoting surfaces.

106, TITLE:     Meta-learning for Few-shot Natural Language Processing: A Survey
http://arxiv.org/abs/2007.09604
AUTHORS:        Wenpeng Yin
COMMENTS:       no submission intent
HIGHLIGHT:      Usually we rely on collecting more auxiliary information or developing a more efficient learning algorithm.

107, TITLE:     Characterization of Potential Drug Treatments for COVID-19 using Social Media Data and Machine Learning
http://arxiv.org/abs/2007.10276
AUTHORS:        Ramya Tekumalla ; Juan M. Banda
COMMENTS:       7 pages, 2 figures and 5 tables
HIGHLIGHT:      In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around potential treatments.

108, TITLE:     Complex Skill Acquisition through Simple Skill Adversarial Imitation Learning
http://arxiv.org/abs/2007.10281
AUTHORS:        Pranay Pasula
COMMENTS:       6 pages, 2 figures
HIGHLIGHT:      Motivated by this line of reasoning, we propose a new algorithm that trains neural network policies on simple, easy-to-learn skills in order to cultivate latent spaces that accelerate adversarial imitation learning of complex, hard-to-learn skills.

109, TITLE:     Relatable Clothing: Detecting Visual Relationships between People and Clothing
http://arxiv.org/abs/2007.10283
AUTHORS:        Thomas Truong ; Svetlana Yanushkevich
COMMENTS:       7 pages, 7 figures, accepted to ICPR 2020
HIGHLIGHT:      We present the release of the Relatable Clothing Dataset which contains 35287 person-clothing pairs and segmentation masks for the development of ``worn'' and ``unworn'' classification models.

110, TITLE:     XingGAN for Person Image Generation
http://arxiv.org/abs/2007.09278
AUTHORS:        Hao Tang ; Song Bai ; Li Zhang ; Philip H. S. Torr ; Nicu Sebe
COMMENTS:       Accepted to ECCV 2020, camera ready (16 pages) + supplementary (6 pages)
HIGHLIGHT:      We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i.e., translating the pose of a given person to a desired one.

111, TITLE:     On Disentangling Spoof Trace for Generic Face Anti-Spoofing
http://arxiv.org/abs/2007.09273
AUTHORS:        Yaojie Liu ; Joel Stehouwer ; Xiaoming Liu
COMMENTS:       To appear in ECCV 2020
HIGHLIGHT:      Prior studies show that the key to face anti-spoofing lies in the subtle image pattern, termed "spoof trace", e.g., color distortion, 3D mask edge, Moire pattern, and many others.

112, TITLE:     OnlineAugment: Online Data Augmentation with Less Domain Knowledge
http://arxiv.org/abs/2007.09271
AUTHORS:        Zhiqiang Tang ; Yunhe Gao ; Leonid Karlinsky ; Prasanna Sattigeri ; Rogerio Feris ; Dimitris Metaxas
COMMENTS:       ECCV2020
HIGHLIGHT:      In this work, we offer an orthogonal online data augmentation scheme together with three new augmentation networks, co-trained with the target learning task.

113, TITLE:     Single View Metrology in the Wild
http://arxiv.org/abs/2007.09529
AUTHORS:        Rui Zhu ; Xingyi Yang ; Yannick Hold-Geoffroy ; Federico Perazzi ; Jonathan Eisenmann ; Kalyan Sunkavalli ; Manmohan Chandraker
COMMENTS:       ECCV 2020, camera-ready version
HIGHLIGHT:      We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground as well as camera parameters of orientation and field of view, using just a monocular image acquired in unconstrained condition.

114, TITLE:     Object Tracking by Least Spatiotemporal Searches
http://arxiv.org/abs/2007.09288
AUTHORS:        Zhiyong Yu ; Lei Han ; Chao Chen ; Wenzhong Guo ; Zhiwen Yu
HIGHLIGHT:      This paper proposes a strategy named IHMs (Intermediate Searching at Heuristic Moments): each step we figure out which moment is the best to search according to a heuristic indicator, then at that moment search locations one by one in descending order of predicted appearing probabilities, until a search hits; iterate this step until we get the object's current location.

115, TITLE:     Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding
http://arxiv.org/abs/2007.09547
AUTHORS:        Songtao He ; Favyen Bastani ; Satvat Jagwani ; Mohammad Alizadeh ; Hari Balakrishnan ; Sanjay Chawla ; Mohamed M. Elshrif ; Samuel Madden ; Amin Sadeghi
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we propose a new method, Sat2Graph, which combines the advantages of the two prior categories into a unified framework.

116, TITLE:     XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup
http://arxiv.org/abs/2007.10252
AUTHORS:        Xingjian Li ; Haoyi Xiong ; Haozhe An ; Chengzhong Xu ; Dejing Dou
HIGHLIGHT:      In this work, we aim to improve the multitask paradigm for deep transfer learning via Cross-domain Mixup (XMixup).

117, TITLE:     Multi-Principal Assistance Games
http://arxiv.org/abs/2007.09540
AUTHORS:        Arnaud Fickinger ; Simon Zhuang ; Dylan Hadfield-Menell ; Stuart Russell
HIGHLIGHT:      In this context we propose a social choice method that uses shared control of a system to combine preference inference with social welfare optimization.

118, TITLE:     Efficient Iterative Solutions to Complex-Valued Nonlinear Least-Squares Problems with Mixed Linear and Antilinear Operators
http://arxiv.org/abs/2007.09281
AUTHORS:        Tae Hyung Kim ; Justin P. Haldar
HIGHLIGHT:      In this work, we describe theory and computational methods that enable mixed linear/antilinear least-squares problems to be solved iteratively using standard linear least-squares tools, while retaining all of the complex-valued structure of the original inverse problem.

119, TITLE:     ContactPose: A Dataset of Grasps with Object Contact and Hand Pose
http://arxiv.org/abs/2007.09545
AUTHORS:        Samarth Brahmbhatt ; Chengcheng Tang ; Christopher D. Twigg ; Charles C. Kemp ; James Hays
COMMENTS:       The European Conference on Computer Vision (ECCV) 2020
HIGHLIGHT:      We introduce ContactPose, the first dataset of hand-object contact paired with hand pose, object pose, and RGB-D images.

120, TITLE:     Gaussian kernel smoothing
http://arxiv.org/abs/2007.09539
AUTHORS:        Moo K. Chung
HIGHLIGHT:      Gaussian kernel smoothing

121, TITLE:     Lagrangian Duality in Reinforcement Learning
http://arxiv.org/abs/2007.09998
AUTHORS:        Pranay Pasula
COMMENTS:       8 pages, 0 figures
HIGHLIGHT:      In this paper, we show show how duality is involved in a variety of RL work, from that which spearheaded the field, such as Richard Bellman's value iteration, to that which was done within just the past few years yet has already had significant impact, such as TRPO, A3C, and GAIL.

122, TITLE:     Feature-level Rating System using Customer Reviews and Review Votes
http://arxiv.org/abs/2007.09513
AUTHORS:        Koteswar Rao Jerripothula ; Ankit Rai ; Kanu Garg ; Yashvardhan Singh Rautela
COMMENTS:       10 pages, 7 figures, and accepted by IEEE Transactions on Computational Social Systems
HIGHLIGHT:      This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision making, both for new customers and manufacturers.

123, TITLE:     FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method
http://arxiv.org/abs/2007.09510
AUTHORS:        Mozhdeh Rouhsedaghat ; Yifan Wang ; Xiou Ge ; Shuowen Hu ; Suya You ; C. -C. Jay Kuo
HIGHLIGHT:      A light-weight low-resolution face gender classification method, called FaceHop, is proposed in this research.

124, TITLE:     Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets
http://arxiv.org/abs/2007.09509
AUTHORS:        Weihong Ren ; Xinchao Wang ; Jiandong Tian ; Yandong Tang ; Antoni B. Chan
COMMENTS:       14 pages
HIGHLIGHT:      In this paper, we propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes.

125, TITLE:     Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering
http://arxiv.org/abs/2007.09990
AUTHORS:        Wonjik Kim ; Asako Kanezaki ; Masayuki Tanaka
COMMENTS:       IEEE Transactions on Image Processing, Accepted in July, 2020
HIGHLIGHT:      The contributions of this study are four-fold.

126, TITLE:     Backpropagated Gradient Representations for Anomaly Detection
http://arxiv.org/abs/2007.09507
AUTHORS:        Gukyeong Kwon ; Mohit Prabhushankar ; Dogancan Temel ; Ghassan AlRegib
COMMENTS:       European Conference on Computer Vision (ECCV) 2020
HIGHLIGHT:      Hence, we propose the utilization of backpropagated gradients as representations to characterize model behavior on anomalies and, consequently, detect such anomalies.

127, TITLE:     Quick Question: Interrupting Users for Microtasks with Reinforcement Learning
http://arxiv.org/abs/2007.09515
AUTHORS:        Bo-Jhang Ho ; Bharathan Balaji ; Mehmet Koseoglu ; Sandeep Sandha ; Siyou Pei ; Mani Srivastava
COMMENTS:       Presented at the 2nd Workshop on Human in the Loop Learning in ICML 2020
HIGHLIGHT:      We model the problem as a Markov decision process and use Advantage Actor Critic algorithm to identify interruptible moments based on context and history of user interactions.

128, TITLE:     GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy
http://arxiv.org/abs/2007.09968
AUTHORS:        Shaoteng Liu ; Lijun Gong ; Kai Ma ; Yefeng Zheng
HIGHLIGHT:      In this paper, we propose a Graph REsidual rE-ranking Network (GREEN) to introduce a class dependency prior into the original image classification network.

129, TITLE:     How are you? Introducing stress-based text tailoring
http://arxiv.org/abs/2007.09970
AUTHORS:        Simone Balloccu ; Ehud Reiter ; Alexandra Johnstone ; Claire Fyfe
HIGHLIGHT:      Healthcare has shown evidence of such dynamics and in this short paper we discuss customising texts based on user stress level, as it could represent a critical factor when it comes to user engagement and behavioural change.

130, TITLE:     Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent
http://arxiv.org/abs/2007.09971
AUTHORS:        Riccardo Barbano ; Chen Zhang ; Simon Arridge ; Bangti Jin
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      In this work, we develop a novel scalable data-driven knowledge-aided computational framework to quantify the model uncertainty via Bayesian neural networks.

131, TITLE:     Complementary Boundary Generator with Scale-Invariant Relation Modeling for Temporal Action Localization: Submission to ActivityNet Challenge 2020
http://arxiv.org/abs/2007.09883
AUTHORS:        Haisheng Su ; Jinyuan Feng ; Hao Shao ; Zhenyu Jiang ; Manyuan Zhang ; Wei Wu ; Yu Liu ; Hongsheng Li ; Junjie Yan
COMMENTS:       Submitted to CVPR workshop of ActivityNet Challenge 2020
HIGHLIGHT:      In this paper, we decouple the temporal action localization task into two stages (i.e. proposal generation and classification) and enrich the proposal diversity through exhaustively exploring the influences of multiple components from different but complementary perspectives.

132, TITLE:     Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
http://arxiv.org/abs/2007.09886
AUTHORS:        Cheng Ouyang ; Carlo Biffi ; Chen Chen ; Turkay Kart ; Huaqi Qiu ; Daniel Rueckert
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      To address this problem we make several contributions: (1) A novel self-supervised FSS framework for medical images in order to eliminate the requirement for annotations during training.

133, TITLE:     MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings
http://arxiv.org/abs/2007.09502
AUTHORS:        Ali Varamesh ; Tinne Tuytelaars
HIGHLIGHT:      We present MIX'EM, a novel solution for unsupervised image classification.

134, TITLE:     Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation
http://arxiv.org/abs/2007.09854
AUTHORS:        Yuexiang Li ; Jiawei Chen ; Xinpeng Xie ; Kai Ma ; Yefeng Zheng
COMMENTS:       Accepted by MICCAI 2020
HIGHLIGHT:      In this paper, we propose a semi-supervised approach to train neural networks with limited labeled data and a large quantity of unlabeled images for medical image segmentation.

135, TITLE:     Frustratingly Hard Evidence Retrieval for QA Over Books
http://arxiv.org/abs/2007.09878
AUTHORS:        Xiangyang Mou ; Mo Yu ; Bingsheng Yao ; Chenghao Yang ; Xiaoxiao Guo ; Saloni Potdar ; Hui Su
COMMENTS:       ACL 2020 NUSE Workshop, 6 pages
HIGHLIGHT:      We formulate BookQA as an open-domain QA task given its similar dependency on evidence retrieval.

136, TITLE:     Cross-View Image Synthesis with Deformable Convolution and Attention Mechanism
http://arxiv.org/abs/2007.09858
AUTHORS:        Hao Ding ; Songsong Wu ; Hao Tang ; Fei Wu ; Guangwei Gao ; Xiao-Yuan Jing
HIGHLIGHT:      In this paper, we propose to use Generative Adversarial Networks(GANs) based on a deformable convolution and attention mechanism to solve the problem of cross-view image synthesis (see Fig.1).

137, TITLE:     Learning Gaussian Instance Segmentation in Point Clouds
http://arxiv.org/abs/2007.09860
AUTHORS:        Shih-Hung Liu ; Shang-Yi Yu ; Shao-Chi Wu ; Hwann-Tzong Chen ; Tyng-Luh Liu
HIGHLIGHT:      This paper presents a novel method for instance segmentation of 3D point clouds.

138, TITLE:     Context-Aware RCNN: A Baseline for Action Detection in Videos
http://arxiv.org/abs/2007.09861
AUTHORS:        Jianchao Wu ; Zhanghui Kuang ; Limin Wang ; Wayne Zhang ; Gangshan Wu
COMMENTS:       ECCV 2020 Camera-ready version
HIGHLIGHT:      Thus, we revisit RCNN for actor-centric action recognition via cropping and resizing image patches around actors before feature extraction with I3D deep network.

139, TITLE:     MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection
http://arxiv.org/abs/2007.09833
AUTHORS:        Fa-Ting Hong ; Xuanteng Huang ; Wei-Hong Li ; Wei-Shi Zheng
HIGHLIGHT:      In this work, we propose casting weakly supervised video highlight detection modeling for a given specific event as a multiple instance ranking network (MINI-Net) learning.

140, TITLE:     Novel Approach to Use HU Moments with Image Processing Techniques for Real Time Sign Language Communication
http://arxiv.org/abs/2007.09859
AUTHORS:        Matheesha Fernando ; Janaka Wijayanayake
HIGHLIGHT:      The main objective of this research is to provide a low cost affordable method of sign language interpretation.

141, TITLE:     Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation
http://arxiv.org/abs/2007.09841
AUTHORS:        Medhini Narasimhan ; Erik Wijmans ; Xinlei Chen ; Trevor Darrell ; Dhruv Batra ; Devi Parikh ; Amanpreet Singh
COMMENTS:       Published at the European Conference on Computer Vision, 2020
HIGHLIGHT:      We introduce a learning-based approach for room navigation using semantic maps.

142, TITLE:     A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping
http://arxiv.org/abs/2007.09824
AUTHORS:        Hmrishav Bandyopadhyay ; Tanmoy Dasgupta ; Nibaran Das ; Mita Nasipuri
HIGHLIGHT:      We propose a supervised Gated and Bifurcated Stacked U-Net module to predict a dewarping grid and create a distortion free image from the input.

143, TITLE:     Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
http://arxiv.org/abs/2007.09835
AUTHORS:        Wei Niu ; Mengshu Sun ; Zhengang Li ; Jou-An Chen ; Jiexiong Guan ; Xipeng Shen ; Yanzhi Wang ; Xue Lin ; Bin Ren
HIGHLIGHT:      This paper proposes RT3D, a model compression and mobile acceleration framework for 3D CNNs, seamlessly integrating neural network weight pruning and compiler code generation techniques.

144, TITLE:     Object-Aware Centroid Voting for Monocular 3D Object Detection
http://arxiv.org/abs/2007.09836
AUTHORS:        Wentao Bao ; Qi Yu ; Yu Kong
COMMENTS:       IROS 2020 Accepted Paper
HIGHLIGHT:      In this paper, we propose an end-to-end trainable monocular 3D object detector without learning the dense depth.

145, TITLE:     Learning to Generate Customized Dynamic 3D Facial Expressions
http://arxiv.org/abs/2007.09805
AUTHORS:        Rolandos Alexandros Potamias ; Jiali Zheng ; Stylianos Ploumpis ; Giorgos Bouritsas ; Evangelos Ververas ; Stefanos Zafeiriou
HIGHLIGHT:      In this paper, we extrapolate those advances to the 3D domain, by studying 3D image-to-video translation with a particular focus on 4D facial expressions.

146, TITLE:     Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution
http://arxiv.org/abs/2007.10087
AUTHORS:        Jacopo Tagliabue ; Bingqing Yu
COMMENTS:       accepted at 2020 SIGIR Workshop On eCommerce
HIGHLIGHT:      We approach counterfactuals in analogy with treatments in formal semantics, explicitly modeling possible outcomes through alternative shopper timelines; in particular, we propose to learn a generative browsing model over a target shop, leveraging the latent space induced by prod2vec embeddings; we show how natural language queries can be effectively represented in the same space and how "search intervention" can be performed to assess causal contribution.

147, TITLE:     Improving Attention-Based Handwritten Mathematical Expression Recognition with Scale Augmentation and Drop Attention
http://arxiv.org/abs/2007.10092
AUTHORS:        Zhe Li ; Lianwen Jin ; Songxuan Lai ; Yecheng Zhu
COMMENTS:       Accepted to appear in ICFHR 2020
HIGHLIGHT:      To address this issue, in this paper, we propose a high-performance HMER model with scale augmentation and drop attention.

148, TITLE:     A Big Data Approach for Sequences Indexing on the Cloud via Burrows Wheeler Transform
http://arxiv.org/abs/2007.10095
AUTHORS:        Mario Randazzo ; Simona E. Rombo
COMMENTS:       Accepted at HELPLINE@ECAI2020
HIGHLIGHT:      Here we propose an algorithm for the computation of Burrows Wheeler transform relying on Big Data technologies, i.e., Apache Spark and Hadoop.

149, TITLE:     EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking
http://arxiv.org/abs/2007.09600
AUTHORS:        Rakshit S. Kothari ; Aayush K. Chaudhary ; Reynold J. Bailey ; Jeff B. Pelz ; Gabriel J. Diaz
HIGHLIGHT:      In this work, we propose training a convolutional neural network to directly segment entire elliptical structures and demonstrate that such a framework is robust to occlusions and offers superior pupil and iris tracking performance (at least 10$\%$ and 24$\%$ increase in pupil and iris center detection rate respectively within a two-pixel error margin) compared to using standard eye parts segmentation for multiple publicly available synthetic segmentation datasets.

150, TITLE:     Relative Pose from Deep Learned Depth and a Single Affine Correspondence
http://arxiv.org/abs/2007.10082
AUTHORS:        Ivan Eichhardt ; Daniel Barath
HIGHLIGHT:      We propose a new approach for combining deep-learned non-metric monocular depth with affine correspondences (ACs) to estimate the relative pose of two calibrated cameras from a single correspondence.

151, TITLE:     Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances
http://arxiv.org/abs/2007.09397
AUTHORS:        Aditya Arun ; C. V. Jawahar ; M. Pawan Kumar
COMMENTS:       To appear at ECCV 2020
HIGHLIGHT:      Unlike previous approaches, we explicitly model the uncertainty in the pseudo label generation process using a conditional distribution.

152, TITLE:     Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications
http://arxiv.org/abs/2007.09979
AUTHORS:        Lijun Gong ; Kai Ma ; Yefeng Zheng
HIGHLIGHT:      In this paper, we explore distractors from the CNN feature space via proposing a neuron intrinsic learning method.

153, TITLE:     Computing regular meromorphic differential forms via Saito's logarithmic residues
http://arxiv.org/abs/2007.09950
AUTHORS:        Katsusuke Nabeshima ; Shinichi Tajima
HIGHLIGHT:      An effective method is introduced for computing logarithmic residues.

154, TITLE:     HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs
http://arxiv.org/abs/2007.09952
AUTHORS:        Hai Victor Habi ; Roy H. Jennings ; Arnon Netzer
HIGHLIGHT:      In this work, we introduce the Hardware Friendly Mixed Precision Quantization Block (HMQ) in order to meet this requirement.

155, TITLE:     Gesture Recognition for Initiating Human-to-Robot Handovers
http://arxiv.org/abs/2007.09945
AUTHORS:        Jun Kwan ; Chinkye Tan ; Akansel Cosgun
HIGHLIGHT:      We pose the handover gesture recognition as a binary classification problem in a single RGB image.

156, TITLE:     The Decidability of Verification under Promising 2.0
http://arxiv.org/abs/2007.09944
AUTHORS:        Parosh Aziz Abdulla ; Mohamed Faouzi Atig ; Adwait Godbole ; Shankaranarayanan Krishna ; Viktor Vafeiadis
HIGHLIGHT:      Therefore, we address, in this paper, the reachability problem for programs running under $\ps$ with relaxed accesses ($\psr$) together with promises.

157, TITLE:     Program algebra for random access machine programs
http://arxiv.org/abs/2007.09946
AUTHORS:        C. A. Middelburg
COMMENTS:       25 pages, Sect. 2--4 are largely shortened versions of Sect. 2--4 of arXiv:1808.04264, which, in turn, draw from preliminary sections of several other papers. arXiv admin note: substantial text overlap with arXiv:1901.08840
HIGHLIGHT:      This paper presents an algebraic theory of instruction sequences with instructions for a random access machine (RAM) as basic instructions, the behaviours produced by the instruction sequences concerned under execution, and the interaction between such behaviours and RAM memories.

158, TITLE:     Interpretable Control by Reinforcement Learning
http://arxiv.org/abs/2007.09964
AUTHORS:        Daniel Hein ; Steffen Limmer ; Thomas A. Runkler
HIGHLIGHT:      In this paper, three recently introduced reinforcement learning (RL) methods are used to generate human-interpretable policies for the cart-pole balancing benchmark.

159, TITLE:     Improving Memory Utilization in Convolutional Neural Network Accelerators
http://arxiv.org/abs/2007.09963
AUTHORS:        Petar Jokic ; Stephane Emery ; Luca Benini
HIGHLIGHT:      This work presents the mathematical model to compute the maximum activations memory overlap and thus the lower bound of on-chip memory needed to perform layer-by-layer processing of convolutional neural networks on memory-limited accelerators.

160, TITLE:     TENet: Triple Excitation Network for Video Salient Object Detection
http://arxiv.org/abs/2007.09943
AUTHORS:        Sucheng Ren ; Chu Han ; Xin Yang ; Guoqiang Han ; Shengfeng He
HIGHLIGHT:      In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations.

161, TITLE:     Incorporating Reinforced Adversarial Learning in Autoregressive Image Generation
http://arxiv.org/abs/2007.09923
AUTHORS:        Kenan E. Ak ; Ning Xu ; Zhe Lin ; Yilin Wang
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      To address these limitations, we propose to use Reinforced Adversarial Learning (RAL) based on policy gradient optimization for autoregressive models.

162, TITLE:     MotionSqueeze: Neural Motion Feature Learning for Video Understanding
http://arxiv.org/abs/2007.09933
AUTHORS:        Heeseung Kwon ; Manjin Kim ; Suha Kwak ; Minsu Cho
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      In this work, we replace external and heavy computation of optical flows with internal and light-weight learning of motion features.

163, TITLE:     Sep-Stereo: Visually Guided Stereophonic Audio Generation by Associating Source Separation
http://arxiv.org/abs/2007.09902
AUTHORS:        Hang Zhou ; Xudong Xu ; Dahua Lin ; Xiaogang Wang ; Ziwei Liu
COMMENTS:       To appear in Proceedings of the European Conference on Computer Vision (ECCV), 2020. Code, models, and video results are available on our webpage: https://hangz-nju-cuhk.github.io/projects/Sep-Stereo
HIGHLIGHT:      To overcome this challenge, we propose to leverage the vastly available mono data to facilitate the generation of stereophonic audio.

164, TITLE:     Reinforcement Communication Learning in Different Social Network Structures
http://arxiv.org/abs/2007.09820
AUTHORS:        Marina Dubova ; Arseny Moskvichev ; Robert Goldstone
HIGHLIGHT:      We examined the effects of social network organization on the properties of communication systems emerging in decentralized, multi-agent reinforcement learning communities.

165, TITLE:     Coinduction Plain and Simple
http://arxiv.org/abs/2007.09909
AUTHORS:        François Bry
HIGHLIGHT:      Coinduction Plain and Simple

166, TITLE:     DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks
http://arxiv.org/abs/2007.09818
AUTHORS:        Hassan Dbouk ; Hetul Sanghvi ; Mahesh Mehendale ; Naresh Shanbhag
COMMENTS:       Published as a conference paper in ECCV 2020
HIGHLIGHT:      To this end, we present a novel fully differentiable non-uniform quantizer that can be seamlessly mapped onto efficient ternary-based dot product engines.

167, TITLE:     Event Prediction in Big Data Era: A Systematic Survey
http://arxiv.org/abs/2007.09815
AUTHORS:        Liang Zhao
HIGHLIGHT:      This survey has presented a comprehensive survey of existing methodologies developed for event prediction methods in the big data era.

168, TITLE:     Multimodal Dialogue State Tracking By QA Approach with Data Augmentation
http://arxiv.org/abs/2007.09903
AUTHORS:        Xiangyang Mou ; Brandyn Sigouin ; Ian Steenstra ; Hui Su
COMMENTS:       AAAI DSTC8 Workshop
HIGHLIGHT:      This paper interprets the AVSD task from an open-domain Question Answering (QA) point of view and proposes a multimodal open-domain QA system to deal with the problem.

169, TITLE:     Wearable camera-based human absolute localization in large warehouses
http://arxiv.org/abs/2007.10066
AUTHORS:        Gaël Écorchard ; Karel Košnar ; Libor Přeučil
COMMENTS:       Conference paper presented at Twelfth International Conference on Machine Vision, 2019
HIGHLIGHT:      The purpose of this paper is to demonstrate an absolute visual localization method working in the challenging environment of an automated warehouse with low intensity of light, massively changing environment and using solely monocular camera placed on the human body.

170, TITLE:     Investigating Bias and Fairness in Facial Expression Recognition
http://arxiv.org/abs/2007.10075
AUTHORS:        Tian Xu ; Jennifer White ; Sinan Kalkan ; Hatice Gunes
HIGHLIGHT:      Therefore, in this work, we undertake a systematic investigation of bias and fairness in facial expression recognition by comparing three different approaches, namely a baseline, an attribute-aware and a disentangled approach, on two well-known datasets, RAF-DB and CelebA.

171, TITLE:     Morphological Skip-Gram: Using morphological knowledge to improve word representation
http://arxiv.org/abs/2007.10055
AUTHORS:        Flávio Santos ; Hendrik Macedo ; Thiago Bispo ; Cleber Zanchetting
COMMENTS:       11 pages
HIGHLIGHT:      In this work, we propose a new method for training word embeddings, and its goal is to replace the FastText bag of character n-grams for a bag of word morphemes through the morphological analysis of the word.

172, TITLE:     It's LeVAsa not LevioSA! Latent Encodings for Valence-Arousal Structure Alignment
http://arxiv.org/abs/2007.10058
AUTHORS:        Surabhi S. Nath ; Vishaal Udandarao ; Jainendra Shukla
COMMENTS:       5 pages, 4 figures and 3 tables
HIGHLIGHT:      We present "LeVAsa", a VAE model that learns implicit structure by aligning the latent space with the VA space.

173, TITLE:     Cephalometric Landmark Regression with Convolutional Neural Networks on 3D Computed Tomography Data
http://arxiv.org/abs/2007.10052
AUTHORS:        Dmitry Lachinov ; Alexandra Getmanskaya ; Vadim Turlapov
HIGHLIGHT:      In this paper, we address the problem of automatic three-dimensional cephalometric analysis.

174, TITLE:     Personalized Speech2Video with 3D Skeleton Regularization and Expressive Body Poses
http://arxiv.org/abs/2007.09198
AUTHORS:        Miao Liao ; Sibo Zhang ; Peng Wang ; Hao Zhu ; Ruigang Yang
HIGHLIGHT:      In this paper, we propose a novel approach to convert given speech audio to a photo-realistic speaking video of a specific person, where the output video has synchronized, realistic, and expressive rich body dynamics. To validate our approach, we collect a dataset with 20 high-quality videos from 1 male and 1 female model reading various documents under different topics.

175, TITLE:     Making Affine Correspondences Work in Camera Geometry Computation
http://arxiv.org/abs/2007.10032
AUTHORS:        Daniel Barath ; Michal Polic ; Wolfgang Förstner ; Torsten Sattler ; Tomas Pajdla ; Zuzana Kukelova
HIGHLIGHT:      We propose a method for refining the local feature geometries by symmetric intensity-based matching, combine uncertainty propagation inside RANSAC with preemptive model verification, show a general scheme for computing uncertainty of minimal solvers results, and adapt the sample cheirality check for homography estimation.

176, TITLE:     Improving Semantic Segmentation via Decoupled Body and Edge Supervision
http://arxiv.org/abs/2007.10035
AUTHORS:        Xiangtai Li ; Xia Li ; Li Zhang ; Guangliang Cheng ; Jianping Shi ; Zhouchen Lin ; Shaohua Tan ; Yunhai Tong
COMMENTS:       accepted by ECCV 2020
HIGHLIGHT:      In this paper, a new paradigm for semantic segmentation is proposed.

177, TITLE:     Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks
http://arxiv.org/abs/2007.09191
AUTHORS:        Sharif Amit Kamran ; Khondker Fariha Hossain ; Alireza Tavakkoli ; Stewart Lee Zuckerbrod
COMMENTS:       8 pages, 4 figures, 2 tables
HIGHLIGHT:      To eradicate the need for an invasive FA extraction procedure, we introduce an Attention-based Generative network that can synthesize Fluorescein Angiography from Fundus images.

178, TITLE:     Knowledge Graph Extraction from Videos
http://arxiv.org/abs/2007.10040
AUTHORS:        Louis Mahon ; Eleonora Giunchiglia ; Bowen Li ; Thomas Lukasiewicz
COMMENTS:       10 pages, 4 figures
HIGHLIGHT:      In this paper, we propose the new task of knowledge graph extraction from videos, i.e., producing a description in the form of a knowledge graph of the contents of a given video.

179, TITLE:     Non-Local Spatial Propagation Network for Depth Completion
http://arxiv.org/abs/2007.10042
AUTHORS:        Jinsun Park ; Kyungdon Joo ; Zhe Hu ; Chi-Kuei Liu ; In So Kweon
COMMENTS:       To appear in ECCV 2020. Project page: https://github.com/zzangjinsun/NLSPN_ECCV20
HIGHLIGHT:      In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion.

180, TITLE:     Voice@SRIB at SemEval-2020 Task [9,12]: Sentiment and Offensiveness detection in Social Media
http://arxiv.org/abs/2007.10021
AUTHORS:        Abhishek Singh ; Surya Pratap Singh Parmar
HIGHLIGHT:      In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks.

181, TITLE:     Learning Sparse Filters in Deep Convolutional Neural Networks with a l1/l2 Pseudo-Norm
http://arxiv.org/abs/2007.10022
AUTHORS:        Anthony Berthelier ; Yongzhe Yan ; Thierry Chateau ; Christophe Blanc ; Stefan Duffner ; Christophe Garcia
COMMENTS:       8 pages, 7 figures, under review for ICPR 2020
HIGHLIGHT:      In this paper, we present a sparsity-inducing regularization term based on the ratio l1/l2 pseudo-norm defined on the filter coefficients.

182, TITLE:     Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization
http://arxiv.org/abs/2007.10026
AUTHORS:        Haibao Yu ; Qi Han ; Jianbo Li ; Jianping Shi ; Guangliang Cheng ; Bin Fan
COMMENTS:       ECCV2020
HIGHLIGHT:      In this paper, we propose a novel soft Barrier Penalty based NAS (BP-NAS) for mixed precision quantization, which ensures all the searched models are inside the valid domain defined by the complexity constraint, thus could return an optimal model under the given constraint by conducting search only one time.

183, TITLE:     Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning
http://arxiv.org/abs/2007.10018
AUTHORS:        Teodora Popordanoska ; Mohit Kumar ; Stefano Teso
COMMENTS:       Accepted at TAILOR workshop at ECAI 2020, the 24th European Conference on Artificial Intelligence
HIGHLIGHT:      Recent work has demonstrated the promise of combining local explanations with active learning for understanding and supervising black-box models.

184, TITLE:     Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation
http://arxiv.org/abs/2007.09375
AUTHORS:        Taekyung Kim ; Changick Kim
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      We propose an SSDA framework that aims to align features via alleviation of the intra-domain discrepancy.

185, TITLE:     SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach
http://arxiv.org/abs/2007.09389
AUTHORS:        Ailing Zeng ; Xiao Sun ; Fuyang Huang ; Minhao Liu ; Qiang Xu ; Stephen Lin
COMMENTS:       European Conference on Computer Vision (ECCV), 2020
HIGHLIGHT:      We propose to take advantage of this fact for better generalization to rare and unseen poses.

186, TITLE:     Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
http://arxiv.org/abs/2007.09384
AUTHORS:        Jiaxi Wu ; Songtao Liu ; Di Huang ; Yunhong Wang
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      To this end, we propose a Multi-scale Positive Sample Refinement (MPSR) approach to enrich object scales in FSOD.

187, TITLE:     Bounding Maps for Universal Lesion Detection
http://arxiv.org/abs/2007.09383
AUTHORS:        Han Li ; Hu Han ; S. Kevin Zhou
COMMENTS:       11 pages, 4 figures
HIGHLIGHT:      In this paper, we propose a box-to-map method to represent a bounding box with three soft continuous maps with bounds in x-, y- and xy- directions.

188, TITLE:     Temporal Complementary Learning for Video Person Re-Identification
http://arxiv.org/abs/2007.09357
AUTHORS:        Ruibing Hou ; Hong Chang ; Bingpeng Ma ; Shiguang Shan ; Xilin Chen
COMMENTS:       17 pages, 6 figures, accepted by ECCV2020
HIGHLIGHT:      This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification.

189, TITLE:     Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
http://arxiv.org/abs/2007.09355
AUTHORS:        Yuyang Qian ; Guojun Yin ; Lu Sheng ; Zixuan Chen ; Jing Shao
COMMENTS:       19 pages, 9 figures, accepted as a POSTER at ECCV2020
HIGHLIGHT:      To introduce frequency into the face forgery detection, we propose a novel Frequency in Face Forgery Network (F3-Net), taking advantages of two different but complementary frequency-aware clues, 1) frequency-aware decomposed image components, and 2) local frequency statistics, to deeply mine the forgery patterns via our two-stream collaborative learning framework.

190, TITLE:     Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing
http://arxiv.org/abs/2007.09365
AUTHORS:        Yajie Xing ; Jingbo Wang ; Gang Zeng
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we propose a novel operator called malleable 2.5D convolution to learn the receptive field along the depth-axis.

191, TITLE:     Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification
http://arxiv.org/abs/2007.09337
AUTHORS:        Wenao Ma ; Shuang Yu ; Kai Ma ; Jiexiang Wang ; Xinghao Ding ; Yefeng Zheng
HIGHLIGHT:      In this paper, we propose a multi-task deep neural network with spatial activation mechanism that is able to segment full retinal vessel, artery and vein simultaneously, without the pre-requirement of vessel segmentation.

192, TITLE:     Drinking from a Firehose: Continual Learning with Web-scale Natural Language
http://arxiv.org/abs/2007.09335
AUTHORS:        Hexiang Hu ; Ozan Sener ; Fei Sha ; Vladlen Koltun
COMMENTS:       Source Code: https://github.com/firehose-dataset/congrad
HIGHLIGHT:      In this paper, we study a natural setting for continual learning on a massive scale. We introduce the problem of personalized online language learning (POLL), which involves fitting personalized language models to a population of users that evolves over time. To facilitate research on POLL, we collect massive datasets of Twitter posts.

193, TITLE:     AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling
http://arxiv.org/abs/2007.09336
AUTHORS:        Wenshuo Ma ; Tingzhong Tian ; Hang Xu ; Yimin Huang ; Zhenguo Li
COMMENTS:       Accepted by ECCV 2020 (spotlight)
HIGHLIGHT:      In this paper, we study the problem of automatically optimizing anchor boxes for object detection.

194, TITLE:     Strudel: Learning Structured-Decomposable Probabilistic Circuits
http://arxiv.org/abs/2007.09331
AUTHORS:        Meihua Dang ; Antonio Vergari ; Guy Van den Broeck
COMMENTS:       13 pages, 9 figures, to be published on PGM2020 (The 10th International Conference on Probabilistic Graphical Models)
HIGHLIGHT:      This paper proposes Strudel, a simple, fast and accurate learning algorithm for structured-decomposable PCs.

195, TITLE:     Hierarchical Contrastive Motion Learning for Video Action Recognition
http://arxiv.org/abs/2007.10321
AUTHORS:        Xitong Yang ; Xiaodong Yang ; Sifei Liu ; Deqing Sun ; Larry Davis ; Jan Kautz
HIGHLIGHT:      In this paper, we present hierarchical contrastive motion learning, a new self-supervised learning framework to extract effective motion representations from raw video frames.

196, TITLE:     Pillar-based Object Detection for Autonomous Driving
http://arxiv.org/abs/2007.10323
AUTHORS:        Yue Wang ; Alireza Fathi ; Abhijit Kundu ; David Ross ; Caroline Pantofaru ; Tom Funkhouser ; Justin Solomon
COMMENTS:       Accepted to ECCV2020
HIGHLIGHT:      We present a simple and flexible object detection framework optimized for autonomous driving.

197, TITLE:     Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation
http://arxiv.org/abs/2007.09257
AUTHORS:        Xingchao Peng ; Yichen Li ; Kate Saenko
COMMENTS:       ECCV 2020 paper
HIGHLIGHT:      To describe and learn relations between different domains, we propose a novel Domain2Vec model to provide vectorial representations of visual domains based on joint learning of feature disentanglement and Gram matrix. To evaluate the effectiveness of our Domain2Vec model, we create two large-scale cross-domain benchmarks.

198, TITLE:     Inter-Homines: Distance-Based Risk Estimation for Human Safety
http://arxiv.org/abs/2007.10243
AUTHORS:        Matteo Fabbri ; Fabio Lanzi ; Riccardo Gasparini ; Simone Calderara ; Lorenzo Baraldi ; Rita Cucchiara
HIGHLIGHT:      In this document, we report our proposal for modeling the risk of possible contagiousity in a given area monitored by RGB cameras where people freely move and interact.

199, TITLE:     Unsupervised Controllable Generation with Self-Training
http://arxiv.org/abs/2007.09250
AUTHORS:        Grigorios G Chrysos ; Jean Kossaifi ; Zhiding Yu ; Anima Anandkumar
HIGHLIGHT:      Instead, we propose an unsupervised framework to learn a distribution of latent codes that control the generator through self-training.

200, TITLE:     Learning Joint Spatial-Temporal Transformations for Video Inpainting
http://arxiv.org/abs/2007.10247
AUTHORS:        Yanhong Zeng ; Jianlong Fu ; Hongyang Chao
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      In this paper, we propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting.

201, TITLE:     Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance
http://arxiv.org/abs/2007.09267
AUTHORS:        Minghua Liu ; Xiaoshuai Zhang ; Hao Su
COMMENTS:       ECCV 2020
HIGHLIGHT:      Instead, we propose to leverage the input point cloud as much as possible, by only adding connectivity information to existing points.

202, TITLE:     3D Human Shape Reconstruction from a Polarization Image
http://arxiv.org/abs/2007.09268
AUTHORS:        Shihao Zou ; Xinxin Zuo ; Yiming Qian ; Sen Wang ; Chi Xu ; Minglun Gong ; Li Cheng
COMMENTS:       ECCV 2020
HIGHLIGHT:      A dedicated two-stage deep learning approach, SfP, is proposed: given a polarization image, stage one aims at inferring the fined-detailed body surface normal; stage two gears to reconstruct the 3D body shape of clothing details.

203, TITLE:     Surface Normal Estimation of Tilted Images via Spatial Rectifier
http://arxiv.org/abs/2007.09264
AUTHORS:        Tien Do ; Khiem Vuong ; Stergios I. Roumeliotis ; Hyun Soo Park
COMMENTS:       16 pages
HIGHLIGHT:      In this paper, we present a spatial rectifier to estimate surface normals of tilted images.

204, TITLE:     Unsupervised Domain Adaptation in the Absence of Source Data
http://arxiv.org/abs/2007.10233
AUTHORS:        Roshni Sahoo ; Divya Shanmugam ; John Guttag
HIGHLIGHT:      We propose an unsupervised method for adapting a source classifier to a target domain that varies from the source domain along natural axes, such as brightness and contrast.

205, TITLE:     Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks
http://arxiv.org/abs/2007.09916
AUTHORS:        Nupur Thakur ; Yuzhen Ding ; Baoxin Li
COMMENTS:       16 pages, 11 figures
HIGHLIGHT:      In this paper, we evaluate the effectiveness of such a retraining strategy in defending against adversarial attacks.

206, TITLE:     Robust Tracking against Adversarial Attacks
http://arxiv.org/abs/2007.09919
AUTHORS:        Shuai Jia ; Chao Ma ; Yibing Song ; Xiaokang Yang
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      We apply the proposed adversarial attack and defense approaches to state-of-the-art deep tracking algorithms.

207, TITLE:     Nengo and low-power AI hardware for robust, embedded neurorobotics
http://arxiv.org/abs/2007.10227
AUTHORS:        Travis DeWolf ; Pawel Jaworski ; Chris Eliasmith
COMMENTS:       11 pages, 2 figures
HIGHLIGHT:      In this paper we demonstrate how the Nengo neural modeling and simulation libraries enable users to quickly develop robotic perception and action neural networks for simulation on neuromorphic hardware using familiar tools, such as Keras and Python.

208, TITLE:     Learning latent representations across multiple data domains using Lifelong VAEGAN
http://arxiv.org/abs/2007.10221
AUTHORS:        Fei Ye ; Adrian G. Bors
COMMENTS:       Accepted as a conference paper at ECCV 2020
HIGHLIGHT:      In this paper, we propose a novel lifelong learning approach, namely the Lifelong VAEGAN (L-VAEGAN), which not only induces a powerful generative replay network but also learns meaningful latent representations, benefiting representation learning.

209, TITLE:     AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images
http://arxiv.org/abs/2007.09178
AUTHORS:        Jordan Ubbens ; Tewodros Ayalew ; Steve Shirtliffe ; Anique Josuttes ; Curtis Pozniak ; Ian Stavness
COMMENTS:       Computer Vision Problems in Plant Phenotyping (CVPPP) in conjunction with ECCV 2020
HIGHLIGHT:      In this paper, we propose a fully unsupervised technique for counting dense objects such as plant organs.

210, TITLE:     iNNk: A Multi-Player Game to Deceive a Neural Network
http://arxiv.org/abs/2007.09177
AUTHORS:        Jennifer Villareale ; Ana Acosta-Ruiz ; Samuel Arcaro ; Thomas Fox ; Evan Freed ; Robert Gray ; Mathias Löwe ; Panote Nuchprayoon ; Aleksanteri Sladek ; Rush Weigelt ; Yifu Li ; Sebastian Risi ; Jichen Zhu
HIGHLIGHT:      This paper presents \textit{iNNK}, a multiplayer drawing game where human players team up against an NN.

211, TITLE:     NPCFace: A Negative-Positive Cooperation Supervision for Training Large-scale Face Recognition
http://arxiv.org/abs/2007.10172
AUTHORS:        Dan Zeng ; Hailin Shi ; Hang Du ; Jun Wang ; Zhen Lei ; Tao Mei
HIGHLIGHT:      In this paper, we study how to make better use of these hard samples for improving the training.

212, TITLE:     Discrete Point Flow Networks for Efficient Point Cloud Generation
http://arxiv.org/abs/2007.10170
AUTHORS:        Roman Klokov ; Edmond Boyer ; Jakob Verbeek
COMMENTS:       In ECCV'20
HIGHLIGHT:      We introduce a latent variable model that builds on normalizing flows with affine coupling layers to generate 3D point clouds of an arbitrary size given a latent shape representation.

213, TITLE:     Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
http://arxiv.org/abs/2007.09180
AUTHORS:        Yuan Tian ; Qin Wang ; Zhiwu Huang ; Wen Li ; Dengxin Dai ; Minghao Yang ; Jun Wang ; Olga Fink
COMMENTS:       To appear in ECCV 2020
HIGHLIGHT:      In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.

214, TITLE:     Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
http://arxiv.org/abs/2007.09183
AUTHORS:        Xiaokang Chen ; Kwan-Yee Lin ; Jingbo Wang ; Wayne Wu ; Chen Qian ; Hongsheng Li ; Gang Zeng
COMMENTS:       Accepted by ECCV 2020
HIGHLIGHT:      In this paper, we propose a unified and efficient Cross-modality Guided Encoder to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.

215, TITLE:     WordCraft: An Environment for Benchmarking Commonsense Agents
http://arxiv.org/abs/2007.09185
AUTHORS:        Minqi Jiang ; Jelena Luketina ; Nantas Nardelli ; Pasquale Minervini ; Philip H. S. Torr ; Shimon Whiteson ; Tim Rocktäschel
HIGHLIGHT:      We evaluate several representation learning methods on this new benchmark and propose a new method for integrating knowledge graphs with an RL agent.



==========Updates to Previous Papers==========
1, TITLE:       Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning
http://arxiv.org/abs/2004.00567
AUTHORS:        Marco Pleines ; Jenia Jitsev ; Mike Preuss ; Frank Zimmer
COMMENTS:       8 pages, 9 figures, 2 tables, under review
HIGHLIGHT:      Whereas the most top performing entries of last year's competition used human demonstrations or reward shaping to learn how to cope with the challenge, we present an approach that performed competitively (placed 7th) but starts completely from scratch by means of Deep Reinforcement Learning with a relatively simple feed-forward deep network structure.

2, TITLE:       A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
http://arxiv.org/abs/2004.01899
AUTHORS:        Xuefei Ning ; Yin Zheng ; Tianchen Zhao ; Yu Wang ; Huazhong Yang
COMMENTS:       15 pages main text; 9 pages appendix
HIGHLIGHT:      This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search.

3, TITLE:       Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
http://arxiv.org/abs/1912.13503
AUTHORS:        Jeffrey O Zhang ; Alexander Sax ; Amir Zamir ; Leonidas Guibas ; Jitendra Malik
COMMENTS:       ECCV 2020 Spotlight. See project website at http://sidetuning.berkeley.edu
HIGHLIGHT:      In this paper, we propose a straightforward alternative: side-tuning.

4, TITLE:       Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models
http://arxiv.org/abs/2005.07310
AUTHORS:        Jize Cao ; Zhe Gan ; Yu Cheng ; Licheng Yu ; Yen-Chun Chen ; Jingjing Liu
COMMENTS:       Accepted by ECCV 2020 as Spotlight
HIGHLIGHT:      To reveal the secrets behind the scene of these powerful models, we present VALUE (Vision-And-Language Understanding Evaluation), a set of meticulously designed probing tasks (e.g., Visual Coreference Resolution, Visual Relation Detection, Linguistic Probing Tasks) generalizable to standard pre-trained V+L models, aiming to decipher the inner workings of multimodal pre-training (e.g., the implicit knowledge garnered in individual attention heads, the inherent cross-modal alignment learned through contextualized multimodal embeddings).

5, TITLE:       A Probabilistic Separation Logic
http://arxiv.org/abs/1907.10708
AUTHORS:        Gilles Barthe ; Justin Hsu ; Kevin Liao
HIGHLIGHT:      We propose a probabilistic separation logic PSL, where separation models probabilistic independence.

6, TITLE:       Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes
http://arxiv.org/abs/1912.01674
AUTHORS:        Chenhongyi Yang ; Vitaly Ablavsky ; Kaihong Wang ; Qi Feng ; Margrit Betke
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this work, we propose a Non-Maximum-Suppression (NMS) algorithm that dramatically improves the detection recall while maintaining high precision in scenes with heavy occlusions.

7, TITLE:       Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
http://arxiv.org/abs/1904.10424
AUTHORS:        Shengcai Liao ; Ling Shao
COMMENTS:       This is the ECCV 2020 version, including the appendix
HIGHLIGHT:      In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps.

8, TITLE:       Spatial Attention Pyramid Network for Unsupervised Domain Adaptation
http://arxiv.org/abs/2003.12979
AUTHORS:        Congcong Li ; Dawei Du ; Libo Zhang ; Longyin Wen ; Tiejian Luo ; Yanjun Wu ; Pengfei Zhu
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      To that end, in this paper, we design a new spatial attention pyramid network for unsupervised domain adaptation.

9, TITLE:       Approximate Inference Turns Deep Networks into Gaussian Processes
http://arxiv.org/abs/1906.01930
AUTHORS:        Mohammad Emtiyaz Khan ; Alexander Immer ; Ehsan Abedi ; Maciej Korzepa
COMMENTS:       published at NeurIPS 2019: https://papers.nips.cc/paper/8573-approximate-inference-turns-deep-networks-into-gaussian-processes.pdf
HIGHLIGHT:      In this paper, we show that certain Gaussian posterior approximations for Bayesian DNNs are equivalent to GP posteriors.

10, TITLE:      Environment-agnostic Multitask Learning for Natural Language Grounded Navigation
http://arxiv.org/abs/2003.00443
AUTHORS:        Xin Wang ; Vihan Jain ; Eugene Ie ; William Yang Wang ; Zornitsa Kozareva ; Sujith Ravi
HIGHLIGHT:      To close the gap between seen and unseen environments, we aim at learning a generalized navigation model from two novel perspectives: (1) we introduce a multitask navigation model that can be seamlessly trained on both Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks, which benefits from richer natural language guidance and effectively transfers knowledge across tasks; (2) we propose to learn environment-agnostic representations for the navigation policy that are invariant among the environments seen during training, thus generalizing better on unseen environments.

11, TITLE:      Speeding up VP9 Intra Encoder with Hierarchical Deep Learning Based Partition Prediction
http://arxiv.org/abs/1906.06476
AUTHORS:        Somdyuti Paul ; Andrey Norkin ; Alan C. Bovik
HIGHLIGHT:      Here, we propose a deep learning based alternative framework to predict the intra-mode superblock partitions in the form of a four-level partition tree, using a hierarchical fully convolutional network (H-FCN). We created a large database of VP9 superblocks and the corresponding partitions to train an H-FCN model, which was subsequently integrated with the VP9 encoder to reduce the intra-mode encoding time.

12, TITLE:      Classifier-agnostic saliency map extraction
http://arxiv.org/abs/1805.08249
AUTHORS:        Konrad Zolna ; Krzysztof J. Geras ; Kyunghyun Cho
HIGHLIGHT:      To address this problem, we propose classifier-agnostic saliency map extraction, which finds all parts of the image that any classifier could use, not just one given in advance.

13, TITLE:      Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
http://arxiv.org/abs/2003.09338
AUTHORS:        Nikita Dvornik ; Cordelia Schmid ; Julien Mairal
COMMENTS:       ECCV'20
HIGHLIGHT:      In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches.

14, TITLE:      Visual-Relation Conscious Image Generation from Structured-Text
http://arxiv.org/abs/1908.01741
AUTHORS:        Duc Minh Vo ; Akihiro Sugimoto
COMMENTS:       accepted at ECCV 2020
HIGHLIGHT:      We propose an end-to-end network for image generation from given structured-text that consists of the visual-relation layout module and the pyramid of GANs, namely stacking-GANs.

15, TITLE:      Connecting Vision and Language with Localized Narratives
http://arxiv.org/abs/1912.03098
AUTHORS:        Jordi Pont-Tuset ; Jasper Uijlings ; Soravit Changpinyo ; Radu Soricut ; Vittorio Ferrari
COMMENTS:       ECCV 2020 Camera Ready
HIGHLIGHT:      We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language.

16, TITLE:      UNITER: UNiversal Image-TExt Representation Learning
http://arxiv.org/abs/1909.11740
AUTHORS:        Yen-Chun Chen ; Linjie Li ; Licheng Yu ; Ahmed El Kholy ; Faisal Ahmed ; Zhe Gan ; Yu Cheng ; Jingjing Liu
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings.

17, TITLE:      PySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AI
http://arxiv.org/abs/1912.09322
AUTHORS:        Sergio G. Burdisso ; Marcelo Errecalde ; Manuel Montes-y-Gómez
HIGHLIGHT:      In this work, we introduce PySS3, a package that implements SS3 and also comes with visualization tools that allow researchers to deploy robust, explainable, and trusty machine learning models for text classification.

18, TITLE:      A quantum active learning algorithm for sampling against adversarial attacks
http://arxiv.org/abs/1912.03283
AUTHORS:        P. A. M. Casares ; M. A. Martin-Delgado
COMMENTS:       Contains an additional dequantization appendix E that does not appear in the published version
HIGHLIGHT:      We propose a theoretical framework that allows us to think of active learning as sampling the most promising new points to be classified, so that the minimum distance between classes can be found and the theorem KH used.

19, TITLE:      On correctness of an n queens program
http://arxiv.org/abs/1909.07479
AUTHORS:        Włodzimierz Drabent
COMMENTS:       14 pages, 1 figure. This version: various modifications, mainly in Section 3
HIGHLIGHT:      Another purpose of the paper is to present an example of precise declarative reasoning about the semantics of a logic program.

20, TITLE:      Learning Interpretable Models for Black-Box Agents
http://arxiv.org/abs/1912.12613
AUTHORS:        Pulkit Verma ; Siddharth Srivastava
COMMENTS:       Newer Updated version. Updated some sections
HIGHLIGHT:      Our main contribution is an algorithm that generates an interrogation policy in the form of a contingent sequence of questions to be posed to the agent.

21, TITLE:      Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
http://arxiv.org/abs/2003.03836
AUTHORS:        Ruoyi Du ; Dongliang Chang ; Ayan Kumar Bhunia ; Jiyang Xie ; Zhanyu Ma ; Yi-Zhe Song ; Jun Guo
HIGHLIGHT:      In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.

22, TITLE:      Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder
http://arxiv.org/abs/2007.06198
AUTHORS:        Gouthaman KV ; Anurag Mittal
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this work, we propose a novel model-agnostic question encoder, Visually-Grounded Question Encoder (VGQE), for VQA that reduces this effect.

23, TITLE:      Coreference Resolution as Query-based Span Prediction
http://arxiv.org/abs/1911.01746
AUTHORS:        Wei Wu ; Fei Wang ; Arianna Yuan ; Fei Wu ; Jiwei Li
HIGHLIGHT:      In this paper, we present an accurate and extensible approach for the coreference resolution task.

24, TITLE:      Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
http://arxiv.org/abs/1910.07153
AUTHORS:        Mingfei Gao ; Zizhao Zhang ; Guo Yu ; Sercan O. Arik ; Larry S. Davis ; Tomas Pfister
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      Here, we propose to unify unlabeled sample selection and model training towards minimizing labeling cost, and make two contributions towards that end.

25, TITLE:      Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational Autoencoders
http://arxiv.org/abs/2001.02408
AUTHORS:        Sarthak Bhagat ; Shagun Uppal ; Zhuyun Yin ; Nengli Lim
HIGHLIGHT:      We introduce MGP-VAE (Multi-disentangled-features Gaussian Processes Variational AutoEncoder), a variational autoencoder which uses Gaussian processes (GP) to model the latent space for the unsupervised learning of disentangled representations in video sequences.

26, TITLE:      MSA-MIL: A deep residual multiple instance learning model based on multi-scale annotation for classification and visualization of glomerular spikes
http://arxiv.org/abs/2007.00858
AUTHORS:        Yilin Chen ; Ming Li ; Yongfei Wu ; Xueyu Liu ; Fang Hao ; Daoxiang Zhou ; Xiaoshuang Zhou ; Chen Wang
HIGHLIGHT:      In this paper, we establish a visualized classification model based on the multi-scale annotation multi-instance learning (MSA-MIL) to achieve glomerular classification and spikes visualization.

27, TITLE:      Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
http://arxiv.org/abs/2004.09363
AUTHORS:        Shervin Minaee ; Rahele Kafieh ; Milan Sonka ; Shakib Yazdani ; Ghazaleh Jamalipour Soufi
HIGHLIGHT:      In this work, we propose a model based on sentence Transformer to detect the main topics of Tweets in recent months.

28, TITLE:      Video Object Segmentation with Episodic Graph Memory Networks
http://arxiv.org/abs/2007.07020
AUTHORS:        Xinkai Lu ; Wenguan Wang ; Martin Danelljan ; Tianfei Zhou ; Jianbing Shen ; Luc Van Gool
COMMENTS:       ECCV2020 Spotlight; website: https://github.com/carrierlxk/GraphMemVOS
HIGHLIGHT:      In this work, a graph memory network is developed to address the novel idea of "learning to update the segmentation model".

29, TITLE:      Visualizing Classification Structure of Large-Scale Classifiers
http://arxiv.org/abs/2007.06068
AUTHORS:        Bilal Alsallakh ; Zhixin Yan ; Shabnam Ghaffarzadegan ; Zeng Dai ; Liu Ren
COMMENTS:       2020 ICML Workshop on Human Interpretability in Machine Learning (WHI 2020)
HIGHLIGHT:      We propose a measure to compute class similarity in large-scale classification based on prediction scores.

30, TITLE:      NODIS: Neural Ordinary Differential Scene Understanding
http://arxiv.org/abs/2001.04735
AUTHORS:        Cong Yuren ; Hanno Ackermann ; Wentong Liao ; Michael Ying Yang ; Bodo Rosenhahn
HIGHLIGHT:      In this work, we interpret that formulation as Ordinary Differential Equation (ODE).

31, TITLE:      SAILenv: Learning in Virtual Visual Environments Made Simple
http://arxiv.org/abs/2007.08224
AUTHORS:        Enrico Meloni ; Luca Pasqualini ; Matteo Tiezzi ; Marco Gori ; Stefano Melacci
COMMENTS:       8 pages, 7 figures, submitted to ICPR 2020
HIGHLIGHT:      In this paper, we present a novel platform, SAILenv, that is specifically designed to be simple and customizable, and that allows researchers to experiment visual recognition in virtual 3D scenes.

32, TITLE:      Semantic Flow for Fast and Accurate Scene Parsing
http://arxiv.org/abs/2002.10120
AUTHORS:        Xiangtai Li ; Ansheng You ; Zhen Zhu ; Houlong Zhao ; Maoke Yang ; Kuiyuan Yang ; Yunhai Tong
COMMENTS:       accepted by ECCV 2020(oral)
HIGHLIGHT:      In this paper, we focus on designing effective method for fast and accurate scene parsing.

33, TITLE:      Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography
http://arxiv.org/abs/2001.00340
AUTHORS:        Yuanyuan Lyu ; Wei-An Lin ; Haofu Liao ; Jingjing Lu ; S. Kevin Zhou
COMMENTS:       accepted by MICCAI 2020
HIGHLIGHT:      In this work, we provide analytical investigation to the issue and propose to address the problem by (1) retaining the metal-affected regions in sinogram and (2) replacing the binarized metal trace with the metal mask projection such that the geometry information of metal implants is encoded.

34, TITLE:      Evolutionary Optimisation of Real-Time Systems and Networks
http://arxiv.org/abs/1905.01888
AUTHORS:        Leandro Soares Indrusiak ; Robert I. Davis ; Piotr Dziurzanski
HIGHLIGHT:      In this paper, we advocate the use of evolutionary approaches to address such optimization problems, aiming to evolve individuals of increased fitness over multiple generations of potential solutions.

35, TITLE:      REVISE: A Tool for Measuring and Mitigating Bias in Image Datasets
http://arxiv.org/abs/2004.07999
AUTHORS:        Angelina Wang ; Arvind Narayanan ; Olga Russakovsky
COMMENTS:       ECCV 2020 Spotlight. Tool available at: https://github.com/princetonvisualai/revise-tool
HIGHLIGHT:      Overall, the key aim of our work is to tackle the machine learning bias problem early in the pipeline.

36, TITLE:      SOLO: Segmenting Objects by Locations
http://arxiv.org/abs/1912.04488
AUTHORS:        Xinlong Wang ; Tao Kong ; Chunhua Shen ; Yuning Jiang ; Lei Li
COMMENTS:       Accepted to Proc. Eur. Conf. Computer Vision (ECCV) 2020. Code is available at https://git.io/AdelaiDet
HIGHLIGHT:      We present a new, embarrassingly simple approach to instance segmentation in images.

37, TITLE:      Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
http://arxiv.org/abs/2004.03037
AUTHORS:        Simon Graham ; David Epstein ; Nasir Rajpoot
HIGHLIGHT:      In this paper, we propose Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework.

38, TITLE:      DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources
http://arxiv.org/abs/2007.06392
AUTHORS:        Amir Sharifi ; Ahmadreza Zibaei ; Mahdi Rezaei
HIGHLIGHT:      In this paper, we propose a CNN-Based pipeline called DeepHAZMAT for detecting and segmenting Hazmats in four steps; 1) optimising the number of input images that are fed into the CNN network, 2) using the YOLOv3-tiny structure to collect the required visual information from the hazardous areas, 3) Hazmat sign segmentation and separation from the background using GrabCut technique, and 4) post-processing the result with morphological operators and convex hull algorithm.

39, TITLE:      Contextual Bandit with Missing Rewards
http://arxiv.org/abs/2007.06368
AUTHORS:        Djallel Bouneffouf ; Sohini Upadhyay ; Yasaman Khazaeni
HIGHLIGHT:      In order to address the missing rewards setting, we propose to combine the standard contextual bandit approach with an unsupervised learning mechanism such as clustering.

40, TITLE:      Generative Low-bitwidth Data Free Quantization
http://arxiv.org/abs/2003.03603
AUTHORS:        Shoukai Xu ; Haokun Li ; Bohan Zhuang ; Jing Liu ; Jiezhang Cao ; Chuangrun Liang ; Mingkui Tan
COMMENTS:       eccv2020 code is available at: https://github.com/xushoukai/GDFQ
HIGHLIGHT:      In this paper, we investigate a simple-yet-effective method called Generative Low-bitwidth Data Free Quantization (GDFQ) to remove the data dependence burden.

41, TITLE:      Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability
http://arxiv.org/abs/2007.03198
AUTHORS:        Utku Ozbulak ; Jonathan Peck ; Wesley De Neve ; Bart Goossens ; Yvan Saeys ; Arnout Van Messem
COMMENTS:       Accepted for the ICML 2020, Workshop on Uncertainty and Robustness in Deep Learning (UDL)
HIGHLIGHT:      In this study, we show that effective regional perturbations can be generated without resorting to complex methods.

42, TITLE:      Analyzing and Improving Neural Networks by Generating Semantic Counterexamples through Differentiable Rendering
http://arxiv.org/abs/1910.00727
AUTHORS:        Lakshya Jain ; Varun Chandrasekaran ; Uyeong Jang ; Wilson Wu ; Andrew Lee ; Andy Yan ; Steven Chen ; Somesh Jha ; Sanjit A. Seshia
HIGHLIGHT:      In this paper, we show how differentiable rendering can be utilized to generate images that are informative, yet realistic, and which can be used to analyze DNN performance and improve its robustness through data augmentation.

43, TITLE:      A Broader Study of Cross-Domain Few-Shot Learning
http://arxiv.org/abs/1912.07200
AUTHORS:        Yunhui Guo ; Noel C. Codella ; Leonid Karlinsky ; James V. Codella ; John R. Smith ; Kate Saenko ; Tajana Rosing ; Rogerio Feris
COMMENTS:       ECCV 2020. Website: https://www.learning-with-limited-labels.com/
HIGHLIGHT:      In this paper, we propose the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, consisting of image data from a diverse assortment of image acquisition methods.

44, TITLE:      Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
http://arxiv.org/abs/2005.10266
AUTHORS:        Liang-Chieh Chen ; Raphael Gontijo Lopes ; Bowen Cheng ; Maxwell D. Collins ; Ekin D. Cubuk ; Barret Zoph ; Hartwig Adam ; Jonathon Shlens
COMMENTS:       Accepted to ECCV 2020
HIGHLIGHT:      In this work, we ask if we may leverage semi-supervised learning in unlabeled video sequences and extra images to improve the performance on urban scene segmentation, simultaneously tackling semantic, instance, and panoptic segmentation.

45, TITLE:      Deep learning achieves perfect anomaly detection on 108,308 retinal images including unlearned diseases
http://arxiv.org/abs/2001.05859
AUTHORS:        Ayaka Suzuki ; Yoshiro Suzuki
HIGHLIGHT:      As long as there is a risk of overlooking a disease with a technique, ophthalmologists must double-check even those images that the technique classifies as normal.

46, TITLE:      Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging
http://arxiv.org/abs/2003.13654
AUTHORS:        Xin Yuan ; Yang Liu ; Jinli Suo ; Qionghai Dai
COMMENTS:       CVPR 2020. Corrected a proof of convergence in previous version
HIGHLIGHT:      In this paper, we develop fast and flexible algorithms for SCI based on the plug-and-play (PnP) framework.

47, TITLE:      Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
http://arxiv.org/abs/2003.13659
AUTHORS:        Xingang Pan ; Xiaohang Zhan ; Bo Dai ; Dahua Lin ; Chen Change Loy ; Ping Luo
COMMENTS:       Accepted to ECCV2020 as oral. 1) Precise GAN-inversion by discriminator-guided generator finetuning. 2) A versatile way for high-quality image restoration and manipulation. Code: https://github.com/XingangPan/deep-generative-prior
HIGHLIGHT:      This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images.

48, TITLE:      Variational Quantum Circuits for Deep Reinforcement Learning
http://arxiv.org/abs/1907.00397
AUTHORS:        Samuel Yen-Chi Chen ; Chao-Han Huck Yang ; Jun Qi ; Pin-Yu Chen ; Xiaoli Ma ; Hsi-Sheng Goan
COMMENTS:       Accepted for publication by IEEE Access
HIGHLIGHT:      The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains.

49, TITLE:      Clustering by Directly Disentangling Latent Space
http://arxiv.org/abs/1911.05210
AUTHORS:        Fei Ding ; Feng Luo ; Yin Yang
COMMENTS:       13 pages, 2 figures, and 7 tables
HIGHLIGHT:      In this paper, we analyze the reasons for performance degeneracy in ClusterGAN.

50, TITLE:      Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples
http://arxiv.org/abs/2004.01459
AUTHORS:        Lili Pan ; Shijie Ai ; Yazhou Ren ; Zenglin Xu
COMMENTS:       17 pages; Accepted by ECCV 2020
HIGHLIGHT:      To this end, this paper proposes a new deep discriminative model--self-paced deep regression forests with consideration on underrepresented examples (SPUDRFs).

51, TITLE:      VINNAS: Variational Inference-based Neural Network Architecture Search
http://arxiv.org/abs/2007.06103
AUTHORS:        Martin Ferianc ; Hongxiang Fan ; Miguel Rodrigues
COMMENTS:       There was a major error in the evaluation. Discrepancy was found in the test data. Therefore the results reported in the paper are no longer valid
HIGHLIGHT:      To address these defects, we present a differentiable variational inference-based NAS method for searching sparse convolutional neural networks.

52, TITLE:      Deep Residual Flow for Out of Distribution Detection
http://arxiv.org/abs/2001.05419
AUTHORS:        Ev Zisselman ; Aviv Tamar
HIGHLIGHT:      In this work, we present a novel approach that improves upon the state-of-the-art by leveraging an expressive density model based on normalizing flows.

53, TITLE:      Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation
http://arxiv.org/abs/1906.06789
AUTHORS:        Annkathrin Krämmer ; Christoph Schöller ; Dhiraj Gulati ; Venkatnarayanan Lakshminarasimhan ; Franz Kurz ; Dominik Rosenbaum ; Claus Lenz ; Alois Knoll
COMMENTS:       This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice
HIGHLIGHT:      In this paper, we propose a hardware and software architecture that enables such a reliable Intelligent Infrastructure System to be built.

54, TITLE:      Longest Common Subsequence on Weighted Sequences
http://arxiv.org/abs/1901.04068
AUTHORS:        Evangelos Kipouridis ; Kostas Tsichlas
COMMENTS:       This is an updated version of the paper accepted at CPM 2020 (Best Paper Award). Includes all proofs that did not fit the page limit, and corrections to some typos
HIGHLIGHT:      In this paper we essentially close the gap between upper and lower bounds by improving both.

55, TITLE:      The Go Transformer: Natural Language Modeling for Game Play
http://arxiv.org/abs/2007.03500
AUTHORS:        Matthew Ciolino ; David Noever ; Josh Kalin
COMMENTS:       8 Pages, 5 Figures, 1 Table
HIGHLIGHT:      This work applies natural language modeling to generate plausible strategic moves in the ancient game of Go.

56, TITLE:      Modeling 3D Shapes by Reinforcement Learning
http://arxiv.org/abs/2003.12397
AUTHORS:        Cheng Lin ; Tingxiang Fan ; Wenping Wang ; Matthias Nießner
COMMENTS:       Accepted to ECCV 2020; Video: https://youtu.be/w5e9g_lvbyE
HIGHLIGHT:      Inspired by such artist-based modeling, we propose a two-step neural framework based on RL to learn 3D modeling policies.

57, TITLE:      Domain Adaptive Imitation Learning
http://arxiv.org/abs/1910.00105
AUTHORS:        Kuno Kim ; Yihong Gu ; Jiaming Song ; Shengjia Zhao ; Stefano Ermon
COMMENTS:       ICML 2020
HIGHLIGHT:      We propose a two step approach to DAIL: alignment followed by adaptation.

58, TITLE:      Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection
http://arxiv.org/abs/2004.13432
AUTHORS:        Wenliang Dai ; Tiezheng Yu ; Zihan Liu ; Pascale Fung
COMMENTS:       Submitted to SemEval-2020 Workshop
HIGHLIGHT:      In this paper, we build an offensive language detection system, which combines multi-task learning with BERT-based models.

59, TITLE:      DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
http://arxiv.org/abs/2004.13458
AUTHORS:        Timo Milbich ; Karsten Roth ; Homanga Bharadhwaj ; Samarth Sinha ; Yoshua Bengio ; Björn Ommer ; Joseph Paul Cohen
COMMENTS:       accepted to ECCV 2020
HIGHLIGHT:      To this end, we propose and study multiple complementary learning tasks, targeting conceptually different data relationships by only resorting to the available training samples and labels of a standard DML setting.

60, TITLE:      Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
http://arxiv.org/abs/2004.01486
AUTHORS:        Tim Scherr ; Katharina Löffler ; Moritz Böhland ; Ralf Mikut
COMMENTS:       22 pages, 14 figures, methods of the team KIT-Sch-GE for the IEEE ISBI 2020 Cell Tracking Challenge
HIGHLIGHT:      In this paper, we present a method for the segmentation of touching cells in microscopy images.

61, TITLE:      QUEST: Quantized embedding space for transferring knowledge
http://arxiv.org/abs/1912.01540
AUTHORS:        Himalaya Jain ; Spyros Gidaris ; Nikos Komodakis ; Patrick Pérez ; Matthieu Cord
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      In this work, we propose a novel way to achieve this goal: by distilling the knowledge through a quantized space.

62, TITLE:      A tetrachotomy of ontology-mediated queries with a covering axiom
http://arxiv.org/abs/2006.04167
AUTHORS:        Olga Gerasimova ; Stanislav Kikot ; Agi Kurucz ; Vladimir Podolskii ; Michael Zakharyaschev
HIGHLIGHT:      In this article, we strip it to the bare bones and focus on conjunctive queries mediated by a simple covering axiom stating that one class is covered by the union of two other classes.

63, TITLE:      Linear Mode Connectivity and the Lottery Ticket Hypothesis
http://arxiv.org/abs/1912.05671
AUTHORS:        Jonathan Frankle ; Gintare Karolina Dziugaite ; Daniel M. Roy ; Michael Carbin
COMMENTS:       Published in ICML 2020. This submission subsumes arXiv:1903.01611 ("Stabilizing the Lottery Ticket Hypothesis" and "The Lottery Ticket Hypothesis at Scale")
HIGHLIGHT:      We use this technique to study iterative magnitude pruning (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained in isolation to full accuracy.

64, TITLE:      Least squares surface reconstruction on arbitrary domains
http://arxiv.org/abs/2007.08661
AUTHORS:        Dizhong Zhu ; William A P Smith
HIGHLIGHT:      We propose a new method for computing numerical derivatives based on 2D Savitzky-Golay filters and K-nearest neighbour kernels.

65, TITLE:      Distilling Knowledge Learned in BERT for Text Generation
http://arxiv.org/abs/1911.03829
AUTHORS:        Yen-Chun Chen ; Zhe Gan ; Yu Cheng ; Jingzhou Liu ; Jingjing Liu
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we present a novel approach, Conditional Masked Language Modeling (C-MLM), to enable the finetuning of BERT on target generation tasks.

66, TITLE:      Controllable Person Image Synthesis with Attribute-Decomposed GAN
http://arxiv.org/abs/2003.12267
AUTHORS:        Yifang Men ; Yiming Mao ; Yuning Jiang ; Wei-Ying Ma ; Zhouhui Lian
COMMENTS:       Accepted by CVPR 2020 (Oral). Project Page: https://menyifang.github.io/projects/ADGAN/ADGAN.html
HIGHLIGHT:      This paper introduces the Attribute-Decomposed GAN, a novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes (e.g., pose, head, upper clothes and pants) provided in various source inputs.

67, TITLE:      Learning Syllogism with Euler Neural-Networks
http://arxiv.org/abs/2007.07320
AUTHORS:        Tiansi Dong ; Chengjiang Li ; Christian Bauckhage ; Juanzi Li ; Stefan Wrobel ; Armin B. Cremers
COMMENTS:       16 pages, 6 figures
HIGHLIGHT:      As basic logic relations are better represented by topological relations between regions, we propose a novel neural network that represents everything as a ball and is able to learn topological configuration as an Euler diagram.

68, TITLE:      Data Generators for Learning Systems Based on RBF Networks
http://arxiv.org/abs/1403.7308
AUTHORS:        Marko Robnik-Šikonja
HIGHLIGHT:      We propose a generator of semi-artificial data with similar properties to the original data which enables development and testing of different data mining algorithms and optimization of their parameters.

69, TITLE:      Self-Supervised Learning by Cross-Modal Audio-Video Clustering
http://arxiv.org/abs/1911.12667
AUTHORS:        Humam Alwassel ; Dhruv Mahajan ; Bruno Korbar ; Lorenzo Torresani ; Bernard Ghanem ; Du Tran
HIGHLIGHT:      Based on this intuition, we propose Cross-Modal Deep Clustering (XDC), a novel self-supervised method that leverages unsupervised clustering in one modality (e.g., audio) as a supervisory signal for the other modality (e.g., video).

70, TITLE:      Gradient-Induced Co-Saliency Detection
http://arxiv.org/abs/2004.13364
AUTHORS:        Zhao Zhang ; Wenda Jin ; Jun Xu ; Ming-Ming Cheng
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, inspired by human behavior, we propose a gradient-induced co-saliency detection (GICD) method. To evaluate the performance of Co-SOD methods on discovering the co-salient object among multiple foregrounds, we construct a challenging CoCA dataset, where each image contains at least one extraneous foreground along with the co-salient object.

71, TITLE:      How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation
http://arxiv.org/abs/2005.01344
AUTHORS:        Junyi Feng ; Songyuan Li ; Yifeng Chen ; Fuxian Huang ; Jiabao Cui ; Xi Li
HIGHLIGHT:      In this paper, we propose to introduce a simple and effective correction stage right after the warping stage to form a framework named Tamed Warping Network (TWNet), aiming to improve the accuracy and robustness of warping-based models.

72, TITLE:      URIE: Universal Image Enhancement for Visual Recognition in the Wild
http://arxiv.org/abs/2007.08979
AUTHORS:        Taeyoung Son ; Juwon Kang ; Namyup Kim ; Sunghyun Cho ; Suha Kwak
COMMENTS:       Accepted as a conference paper at ECCV 2020
HIGHLIGHT:      To tackle this issue, we present a Universal and Recognition-friendly Image Enhancement network, dubbed URIE, which is attached in front of existing recognition models and enhances distorted input to improve their performance without retraining them.

73, TITLE:      PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks
http://arxiv.org/abs/2004.07464
AUTHORS:        Wenwen Yu ; Ning Lu ; Xianbiao Qi ; Ping Gong ; Rong Xiao
COMMENTS:       Accepted by ICPR2020. Code at https://github.com/wenwenyu/PICK-pytorch
HIGHLIGHT:      In this paper, we introduce PICK, a framework that is effective and robust in handling complex documents layout for KIE by combining graph learning with graph convolution operation, yielding a richer semantic representation containing the textual and visual features and global layout without ambiguity.

74, TITLE:      Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models
http://arxiv.org/abs/2007.05791
AUTHORS:        Yue Liu ; Hossein Azizpour ; Fredrik Strand ; Kevin Smith
COMMENTS:       Accepted by MICCAI 2020 (Medical Image Computing and Computer Assisted Intervention)
HIGHLIGHT:      With this in mind, we trained networks using three different criteria to select the positive training data (i.e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis.

75, TITLE:      CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task
http://arxiv.org/abs/2006.01205
AUTHORS:        Sirwe Saeedi ; Aliakbar Panahi ; Seyran Saeedi ; Alvis C Fong
COMMENTS:       6 pages, 1 figure, 2 tables, SemEval -2020, Commonsense Reasoning and Natural Language Processing
HIGHLIGHT:      In this paper, we investigate a commonsense inference task that unifies natural language understanding and commonsense reasoning.

76, TITLE:      A Comparative Study on Early Detection of COVID-19 from Chest X-Ray Images
http://arxiv.org/abs/2006.05332
AUTHORS:        Mete Ahishali ; Aysen Degerli ; Mehmet Yamac ; Serkan Kiranyaz ; Muhammad E. H. Chowdhury ; Khalid Hameed ; Tahir Hamid ; Rashid Mazhar ; Moncef Gabbouj
COMMENTS:       11 pages
HIGHLIGHT:      In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 175 early-stage COVID-19 Pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 1579 samples for control (normal) class.

77, TITLE:      Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
http://arxiv.org/abs/1907.02044
AUTHORS:        Francesco Croce ; Matthias Hein
HIGHLIGHT:      We propose in this paper a new white-box adversarial attack wrt the $l_p$-norms for $p \in \{1,2,\infty\}$ aiming at finding the minimal perturbation necessary to change the class of a given input.

78, TITLE:      UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection
http://arxiv.org/abs/2005.00050
AUTHORS:        Andrey Kutuzov ; Mario Giulianelli
COMMENTS:       To appear in Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020)
HIGHLIGHT:      We apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1.

79, TITLE:      Energy-Based Models for Deep Probabilistic Regression
http://arxiv.org/abs/1909.12297
AUTHORS:        Fredrik K. Gustafsson ; Martin Danelljan ; Goutam Bhat ; Thomas B. Schön
COMMENTS:       ECCV 2020. Code is available at https://github.com/fregu856/ebms_regression
HIGHLIGHT:      We address these issues by proposing a general and conceptually simple regression method with a clear probabilistic interpretation.

80, TITLE:      Unsupervised Representation Learning by Predicting Random Distances
http://arxiv.org/abs/1912.12186
AUTHORS:        Hu Wang ; Guansong Pang ; Chunhua Shen ; Congbo Ma
HIGHLIGHT:      To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space.

81, TITLE:      A Simple Approach to Case-Based Reasoning in Knowledge Bases
http://arxiv.org/abs/2006.14198
AUTHORS:        Rajarshi Das ; Ameya Godbole ; Shehzaad Dhuliawala ; Manzil Zaheer ; Andrew McCallum
HIGHLIGHT:      We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classical artificial intelligence (AI).

82, TITLE:      Learning to Generate Grounded Visual Captions without Localization Supervision
http://arxiv.org/abs/1906.00283
AUTHORS:        Chih-Yao Ma ; Yannis Kalantidis ; Ghassan AlRegib ; Peter Vajda ; Marcus Rohrbach ; Zsolt Kira
COMMENTS:       ECCV 2020. Code is available at https://github.com/chihyaoma/cyclical-visual-captioning
HIGHLIGHT:      In this work, we help the model to achieve this via a novel cyclical training regimen that forces the model to localize each word in the image after the sentence decoder generates it, and then reconstruct the sentence from the localized image region(s) to match the ground-truth.

83, TITLE:      Online Invariance Selection for Local Feature Descriptors
http://arxiv.org/abs/2007.08988
AUTHORS:        Rémi Pautrat ; Viktor Larsson ; Martin R. Oswald ; Marc Pollefeys
COMMENTS:       27 pages, Accepted at ECCV 2020 (Oral)
HIGHLIGHT:      To be invariant, or not to be invariant: that is the question formulated in this work about local descriptors.

84, TITLE:      Constructing Driver Hamiltonians for Several Linear Constraints
http://arxiv.org/abs/2006.12028
AUTHORS:        Hannes Leipold ; Federico M. Spedalieri
COMMENTS:       20 pages, 2 figures
HIGHLIGHT:      In this work, we develop a simple and intuitive algebraic framework for reasoning about the commutation of Hamiltonians with linear constraints - one that allows us to classify the complexity of finding a driver Hamiltonian for an arbitrary set of constraints as NP-hard.

85, TITLE:      Content-Aware Unsupervised Deep Homography Estimation
http://arxiv.org/abs/1909.05983
AUTHORS:        Jirong Zhang ; Chuan Wang ; Shuaicheng Liu ; Lanpeng Jia ; Nianjin Ye ; Jue Wang ; Ji Zhou ; Jian Sun
COMMENTS:       Accepted by ECCV 2020 (Oral, Top 2%, 3 over 3 Strong Accepts). Jirong Zhang and Chuan Wang are joint first authors, and Shuaicheng Liu is the corresponding author
HIGHLIGHT:      To overcome these problems, in this work we propose an unsupervised deep homography method with a new architecture design.

86, TITLE:      SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
http://arxiv.org/abs/2007.08954
AUTHORS:        Jinming Zhao ; Ming Liu ; Longxiang Gao ; Yuan Jin ; Lan Du ; He Zhao ; He Zhang ; Gholamreza Haffari
COMMENTS:       accepted to SIGIR 2020
HIGHLIGHT:      Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains.

87, TITLE:      Improving Interpretability of Word Embeddings by Generating Definition and Usage
http://arxiv.org/abs/1912.05898
AUTHORS:        Haitong Zhang ; Yongping Du ; Jiaxin Sun ; Qingxiao Li
HIGHLIGHT:      We propose a novel framework for definition modeling, which can generate reasonable and understandable context-dependent definitions.

88, TITLE:      Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
http://arxiv.org/abs/1809.01316
AUTHORS:        Donghyeon Kim ; Jinhyuk Lee ; Donghee Choi ; Jaehoon Choi ; Jaewoo Kang
COMMENTS:       CIKM 2018
HIGHLIGHT:      In this paper, we propose Neural Event Scheduling Assistant (NESA) which learns user preferences and understands calendar contexts, directly from raw online calendars for fully automated and highly effective event scheduling.

89, TITLE:      Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
http://arxiv.org/abs/2007.08801
AUTHORS:        Hang Wang ; Minghao Xu ; Bingbing Ni ; Wenjun Zhang
COMMENTS:       Accepted by ECCV 2020. Code is available at \url{https://github.com/ChrisAllenMing/LtC-MSDA}
HIGHLIGHT:      To mitigate these problems, we propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework via exploring interactions among domains.

90, TITLE:      Image De-Quantization Using Generative Models as Priors
http://arxiv.org/abs/2007.07923
AUTHORS:        Kalliopi Basioti ; George V. Moustakides
HIGHLIGHT:      Our goal in this work is to develop a de-quantization mechanism through a rigorous mathematical analysis which is based on the classical statistical estimation theory.

91, TITLE:      Learning to Generate Novel Domains for Domain Generalization
http://arxiv.org/abs/2007.03304
AUTHORS:        Kaiyang Zhou ; Yongxin Yang ; Timothy Hospedales ; Tao Xiang
COMMENTS:       To appear in ECCV'20
HIGHLIGHT:      This paper focuses on domain generalization (DG), the task of learning from multiple source domains a model that generalizes well to unseen domains.

92, TITLE:      Agora: A Unified Asset Ecosystem Going Beyond Marketplaces and Cloud Services
http://arxiv.org/abs/1909.03026
AUTHORS:        Jonas Traub ; Jorge-Arnulfo Quiané-Ruiz ; Zoi Kaoudi ; Volker Markl
HIGHLIGHT:      In this vision paper, we present Agora, a unified asset ecosystem.

93, TITLE:      Deformable Style Transfer
http://arxiv.org/abs/2003.11038
AUTHORS:        Sunnie S. Y. Kim ; Nicholas Kolkin ; Jason Salavon ; Gregory Shakhnarovich
COMMENTS:       ECCV 2020 (21 pages, 11 figures including the supplementary material)
HIGHLIGHT:      We propose deformable style transfer (DST), an optimization-based approach that jointly stylizes the texture and geometry of a content image to better match a style image.

94, TITLE:      Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations
http://arxiv.org/abs/2007.08749
AUTHORS:        Benjamin Schloss ; Sandeep Konam
COMMENTS:       22 pages,1 figure
HIGHLIGHT:      In the current paper, we focus on two tasks: classifying utterances from medical conversations according to (i)the SOAP section and (ii) the speaker role, both fundamental building blocks along the path towards an end-to-end, automated SOAP note for medical conversations.

95, TITLE:      Automatic Image Labelling at Pixel Level
http://arxiv.org/abs/2007.07415
AUTHORS:        Xiang Zhang ; Wei Zhang ; Jinye Peng ; Jianping Fan
HIGHLIGHT:      To alleviate the burden of manual image labelling, we propose an interesting learning approach to generate pixel-level image labellings automatically.

96, TITLE:      Improvements to Target-Based 3D LiDAR to Camera Calibration
http://arxiv.org/abs/1910.03126
AUTHORS:        Jiunn-Kai Huang ; Jessy W. Grizzle
HIGHLIGHT:      This paper proposes (1) the use of targets of known dimension and geometry to ameliorate target pose estimation in face of the quantization and systematic errors inherent in a LiDAR image of a target, and (2) a fitting method for the LiDAR to monocular camera transformation that fundamentally assumes the camera image data is the most accurate information in one's possession.

97, TITLE:      Localization Uncertainty Estimation for Anchor-Free Object Detection
http://arxiv.org/abs/2006.15607
AUTHORS:        Youngwan Lee ; Joong-won Hwang ; Hyung-Il Kim ; Kimin Yun ; Joungyoul Park
HIGHLIGHT:      Therefore, we propose a new object detector called Gaussian-FCOS that estimates the localization uncertainty based on an anchor-free detector that captures the uncertainty of similar property with four directions of box offsets (left, right, top, bottom) and avoids the anchor tuning.

98, TITLE:      SOLAR: Second-Order Loss and Attention for Image Retrieval
http://arxiv.org/abs/2001.08972
AUTHORS:        Tony Ng ; Vassileios Balntas ; Yurun Tian ; Krystian Mikolajczyk
COMMENTS:       in ECCV 2020
HIGHLIGHT:      In this work, we explore two second-order components.

99, TITLE:      Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification
http://arxiv.org/abs/2003.07833
AUTHORS:        Sanath Narayan ; Akshita Gupta ; Fahad Shahbaz Khan ; Cees G. M. Snoek ; Ling Shao
COMMENTS:       Accepted for publication at ECCV 2020
HIGHLIGHT:      We propose to enforce semantic consistency at all stages of (generalized) zero-shot learning: training, feature synthesis and classification.

100, TITLE:     Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos
http://arxiv.org/abs/1912.05971
AUTHORS:        Yucheng Zhu ; Xiongkuo Min ; DanDan Zhu ; Ke Gu ; Jiantao Zhou ; Guangtao Zhai ; Xiaokang Yang ; Wenjun Zhang
HIGHLIGHT:      To bridge the gap between the saliency prediction on real-world scene and on scene augmented by virtual information, we construct the ARVR saliency dataset with 12 diverse videos viewed by 20 people.

101, TITLE:     TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge
http://arxiv.org/abs/2003.13260
AUTHORS:        Junyi Feng ; Songyuan Li ; Xi Li ; Fei Wu ; Qi Tian ; Ming-Hsuan Yang ; Haibin Ling
HIGHLIGHT:      In this paper, we rethink this problem from a different viewpoint: using knowledge contained in compressed videos.

102, TITLE:     Learning Object Depth from Camera Motion and Video Object Segmentation
http://arxiv.org/abs/2007.05676
AUTHORS:        Brent A. Griffin ; Jason J. Corso
COMMENTS:       ECCV 2020
HIGHLIGHT:      We demonstrate our approach across domains using a robot camera to locate objects from the YCB dataset and a vehicle camera to locate obstacles while driving.

103, TITLE:     Model-based occlusion disentanglement for image-to-image translation
http://arxiv.org/abs/2004.01071
AUTHORS:        Fabio Pizzati ; Pietro Cerri ; Raoul de Charette
COMMENTS:       ECCV 2020
HIGHLIGHT:      Model-based occlusion disentanglement for image-to-image translation

104, TITLE:     Accelerating CNN Training by Pruning Activation Gradients
http://arxiv.org/abs/1908.00173
AUTHORS:        Xucheng Ye ; Pengcheng Dai ; Junyu Luo ; Xin Guo ; Yingjie Qi ; Jianlei Yang ; Yiran Chen
COMMENTS:       accepted by ECCV 2020
HIGHLIGHT:      Hence, we consider pruning these very small gradients randomly to accelerate CNN training according to the statistical distribution of activation gradients.

105, TITLE:     Long-term Human Motion Prediction with Scene Context
http://arxiv.org/abs/2007.03672
AUTHORS:        Zhe Cao ; Hang Gao ; Karttikeya Mangalam ; Qi-Zhi Cai ; Minh Vo ; Jitendra Malik
COMMENTS:       ECCV 2020 Oral. Dataset & Code: https://github.com/ZheC/GTA-IM-Dataset Video: https://people.eecs.berkeley.edu/~zhecao/hmp/index.html
HIGHLIGHT:      In this work, we propose a novel three-stage framework that exploits scene context to tackle this task.

106, TITLE:     LAXARY: A Trustworthy Explainable Twitter Analysis Model for Post-Traumatic Stress Disorder Assessment
http://arxiv.org/abs/2003.07433
AUTHORS:        Mohammad Arif Ul Alam ; Dhawal Kapadia
COMMENTS:       Accepted in SmartComp 2020 (SmartSys)
HIGHLIGHT:      To answer the above question, we propose, LAXARY (Linguistic Analysis-based Exaplainable Inquiry) model, a novel Explainable Artificial Intelligent (XAI) model to detect and represent PTSD assessment of twitter users using a modified Linguistic Inquiry and Word Count (LIWC) analysis.

107, TITLE:     Value of structural health information in partially observable stochastic environments
http://arxiv.org/abs/1912.12534
AUTHORS:        C. P. Andriotis ; K. G. Papakonstantinou ; E. N. Chatzi
HIGHLIGHT:      In this work, we introduce and study the theoretical and computational foundations of the above metrics within the context of Partially Observable Markov Decision Processes (POMDPs), thus alluding to a broad class of decision-making problems of partially observable stochastic deteriorating environments that can be modeled as POMDPs.

108, TITLE:     Improving Face Recognition from Hard Samples via Distribution Distillation Loss
http://arxiv.org/abs/2002.03662
AUTHORS:        Yuge Huang ; Pengcheng Shen ; Ying Tai ; Shaoxin Li ; Xiaoming Liu ; Jilin Li ; Feiyue Huang ; Rongrong Ji
COMMENTS:       ECCV2020
HIGHLIGHT:      To improve the performance on those hard samples for general tasks, we propose a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is a simple, effective and generic for various types of facial variations.

109, TITLE:     Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
http://arxiv.org/abs/2005.08829
AUTHORS:        Chen Wang ; Wenshan Wang ; Yuheng Qiu ; Yafei Hu ; Sebastian Scherer
COMMENTS:       Oral paper in ECCV 2020
HIGHLIGHT:      In this paper, we explore the problem of interesting scene prediction for mobile robots.

110, TITLE:     RGB-D SLAM in Dynamic Environments Using Point Correlations
http://arxiv.org/abs/1811.03217
AUTHORS:        Weichen Dai ; Yu Zhang ; Ping Li ; Zheng Fang ; Sebastian Scherer
COMMENTS:       18 pages
HIGHLIGHT:      In this paper, a simultaneous localization and mapping (SLAM) method that eliminates the influence of moving objects in dynamic environments is proposed.

111, TITLE:     On the Equivalence of Forward Mode Automatic Differentiation and Symbolic Differentiation
http://arxiv.org/abs/1904.02990
AUTHORS:        Soeren Laue
HIGHLIGHT:      We show that forward mode automatic differentiation and symbolic differentiation are equivalent in the sense that they both perform the same operations when computing derivatives.

112, TITLE:     Salient Object Detection in the Deep Learning Era: An In-Depth Survey
http://arxiv.org/abs/1904.09146
AUTHORS:        Wenguan Wang ; Qiuxia Lai ; Huazhu Fu ; Jianbing Shen ; Haibin Ling ; Ruigang Yang
COMMENTS:       https://github.com/wenguanwang/SODsurvey
HIGHLIGHT:      To facilitate the in-depth understanding of deep SODs, in this paper, we provide a comprehensive survey covering various aspects ranging from algorithm taxonomy to unsolved open issues.

113, TITLE:     PointAR: Efficient Lighting Estimation for Mobile Augmented Reality
http://arxiv.org/abs/2004.00006
AUTHORS:        Yiqin Zhao ; Tian Guo
COMMENTS:       Accepted to 16th European Conference On Computer Vision (ECCV'20)
HIGHLIGHT:      We propose an efficient lighting estimation pipeline that is suitable to run on modern mobile devices, with comparable resource complexities to state-of-the-art mobile deep learning models.

114, TITLE:     Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain
http://arxiv.org/abs/1812.09431
AUTHORS:        Chi Zhang ; Xiaohan Duan ; Linyuan Wang ; Yongli Li ; Bin Yan ; Guoen Hu ; Ruyuan Zhang ; Li Tong
HIGHLIGHT:      Here, we leverage adversarial noise (AN) and adversarial interference (AI) images to quantify the consistency between neural representations and perceptual outcomes in the two systems.

115, TITLE:     FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
http://arxiv.org/abs/1901.02212
AUTHORS:        Umur Aybars Ciftci ; Ilke Demir
COMMENTS:       To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), accepted July 2020. Dataset: http://bit.ly/FakeCatcher
HIGHLIGHT:      We present a novel approach to detect synthetic content in portrait videos, as a preventive solution for the emerging threat of deep fakes. Lastly, we release an "in the wild" dataset of fake portrait videos that we collected as a part of our evaluation process.

116, TITLE:     JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans
http://arxiv.org/abs/2005.12209
AUTHORS:        Fengze Liu ; Jinzheng Cai ; Yuankai Huo ; Chi-Tung Cheng ; Ashwin Raju ; Dakai Jin ; Jing Xiao ; Alan Yuille ; Le Lu ; ChienHung Liao ; Adam P Harrison
COMMENTS:       accepted to ECCV 2020
HIGHLIGHT:      In this work, we propose a novel multi-task learning system, JSSR, based on an end-to-end 3D convolutional neural network that is composed of a generator, a registration and a segmentation component.

117, TITLE:     GRNet: Gridding Residual Network for Dense Point Cloud Completion
http://arxiv.org/abs/2006.03761
AUTHORS:        Haozhe Xie ; Hongxun Yao ; Shangchen Zhou ; Jiageng Mao ; Shengping Zhang ; Wenxiu Sun
COMMENTS:       ECCV 2020
HIGHLIGHT:      To solve this problem, we introduce 3D grids as intermediate representations to regularize unordered point clouds.

118, TITLE:     High Fidelity Face Manipulation with Extreme Poses and Expressions
http://arxiv.org/abs/1903.12003
AUTHORS:        Chaoyou Fu ; Yibo Hu ; Xiang Wu ; Guoli Wang ; Qian Zhang ; Ran He
HIGHLIGHT:      In this paper, we propose a novel framework that simplifies face manipulation into two correlated stages: a boundary prediction stage and a disentangled face synthesis stage.

119, TITLE:     Learning Canonical Representations for Scene Graph to Image Generation
http://arxiv.org/abs/1912.07414
AUTHORS:        Roei Herzig ; Amir Bar ; Huijuan Xu ; Gal Chechik ; Trevor Darrell ; Amir Globerson
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this work, we show that one limitation of current methods is their inability to capture semantic equivalence in graphs.

120, TITLE:     Thirty-seven years of relational Hoare logic: remarks on its principles and history
http://arxiv.org/abs/2007.06421
AUTHORS:        David A. Naumann
COMMENTS:       Submitted to ISOLA 2020. Version2: fix typos, minor clarifications, add a citation
HIGHLIGHT:      Thirty-seven years of relational Hoare logic: remarks on its principles and history

121, TITLE:     Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
http://arxiv.org/abs/2004.12276
AUTHORS:        Menglin Jia ; Mengyun Shi ; Mikhail Sirotenko ; Yin Cui ; Claire Cardie ; Bharath Hariharan ; Hartwig Adam ; Serge Belongie
COMMENTS:       eccv2020
HIGHLIGHT:      In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes).

122, TITLE:     Deformable spatial propagation network for depth completion
http://arxiv.org/abs/2007.04251
AUTHORS:        Zheyuan Xu ; Hongche Yin ; Jian Yao
COMMENTS:       5 pages, 3 figures
HIGHLIGHT:      To tackle this issue, in this paper, we propose a deformable spatial propagation network (DSPN) to adaptively generates different receptive field and affinity matrix for each pixel.

123, TITLE:     A Strong XOR Lemma for Randomized Query Complexity
http://arxiv.org/abs/2007.05580
AUTHORS:        Joshua Brody ; Jae Tak Kim ; Peem Lerdputtipongporn ; Hariharan Srinivasulu
COMMENTS:       9 pages
HIGHLIGHT:      We give a strong direct sum theorem for computing $xor \circ g$.

124, TITLE:     Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis
http://arxiv.org/abs/2007.01189
AUTHORS:        Avinash Madasu ; Vijjini Anvesh Rao
COMMENTS:       Accepted at 25th International Conference on Pattern Recognition, January 2021, Milan, Italy
HIGHLIGHT:      We propose a model-independent framework - Sequential Domain Adaptation (SDA).

125, TITLE:     Predicting Landslides Using Locally Aligned Convolutional Neural Networks
http://arxiv.org/abs/1911.04651
AUTHORS:        Ainaz Hajimoradlou ; Gioachino Roberti ; David Poole
COMMENTS:       Published in IJCAI 2020
HIGHLIGHT:      We propose a model called Locally Aligned Convolutional Neural Network, LACNN, that follows the ground surface at multiple scales to predict possible landslide occurrence for a single point. To validate our method, we created a standardized dataset of georeferenced images consisting of the heterogeneous features as inputs, and compared our method to several baselines, including linear regression, a neural network, and a convolutional network, using log-likelihood error and Receiver Operating Characteristic curves on the test set.

126, TITLE:     Federated Generative Adversarial Learning
http://arxiv.org/abs/2005.03793
AUTHORS:        Chenyou Fan ; Ping Liu
HIGHLIGHT:      To handle those problems, we propose a novel generative learning scheme utilizing a federated learning framework.

127, TITLE:     Quantification of Tomographic Patterns associated with COVID-19 from Chest CT
http://arxiv.org/abs/2004.01279
AUTHORS:        Shikha Chaganti ; Abishek Balachandran ; Guillaume Chabin ; Stuart Cohen ; Thomas Flohr ; Bogdan Georgescu ; Philippe Grenier ; Sasa Grbic ; Siqi Liu ; François Mellot ; Nicolas Murray ; Savvas Nicolaou ; William Parker ; Thomas Re ; Pina Sanelli ; Alexander W. Sauter ; Zhoubing Xu ; Youngjin Yoo ; Valentin Ziebandt ; Dorin Comaniciu
HIGHLIGHT:      Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

128, TITLE:     Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting
http://arxiv.org/abs/2007.06759
AUTHORS:        Bindita Chaudhuri ; Noranart Vesdapunt ; Linda Shapiro ; Baoyuan Wang
COMMENTS:       ECCV 2020 (spotlight), webpage: https://homes.cs.washington.edu/~bindita/personalizedfacemodeling.html
HIGHLIGHT:      We propose an end-to-end framework that jointly learns a personalized face model per user and per-frame facial motion parameters from a large corpus of in-the-wild videos of user expressions.

129, TITLE:     Learning to Accelerate Decomposition for Multi-Directional 3D Printing
http://arxiv.org/abs/2004.03450
AUTHORS:        Chenming Wu ; Yong-Jin Liu ; Charlie C. L. Wang
COMMENTS:       8 pages, accepted by IEEE Robotics and Automation Letters 2020
HIGHLIGHT:      Recent work proposed a beam-guided search algorithm to find an optimized sequence of plane-clipping, which gives volume decomposition of a given 3D model.

130, TITLE:     JNR: Joint-based Neural Rig Representation for Compact 3D Face Modeling
http://arxiv.org/abs/2007.06755
AUTHORS:        Noranart Vesdapunt ; Mitch Rundle ; HsiangTao Wu ; Baoyuan Wang
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we introduce a novel approach to learn a 3D face model using a joint-based face rig and a neural skinning network.

131, TITLE:     A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
http://arxiv.org/abs/1812.00315
AUTHORS:        Xinyi Zhou ; Reza Zafarani
COMMENTS:       ACM Computing Surveys (CSUR), 37 pages
HIGHLIGHT:      In particular, we identify and detail related fundamental theories across various disciplines to encourage interdisciplinary research on fake news.

132, TITLE:     From Symmetry to Geometry: Tractable Nonconvex Problems
http://arxiv.org/abs/2007.06753
AUTHORS:        Yuqian Zhang ; Qing Qu ; John Wright
COMMENTS:       review paper, 34 pages, 10 figures
HIGHLIGHT:      The goal of this survey is to highlight a class of tractable nonconvex problems, which can be understood through the lens of symmetries.

133, TITLE:     DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation
http://arxiv.org/abs/2004.02164
AUTHORS:        Xuefei Ning ; Tianchen Zhao ; Wenshuo Li ; Peng Lei ; Yu Wang ; Huazhong Yang
COMMENTS:       The first two authors contribute equally
HIGHLIGHT:      In this paper, we propose Differentiable Sparsity Allocation (DSA), an efficient end-to-end budgeted pruning flow.

134, TITLE:     Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks
http://arxiv.org/abs/2007.03207
AUTHORS:        Yan Liu ; Lingqiao Liu ; Peng Wang ; Pingping Zhang ; Yinjie Lei
COMMENTS:       To be appeared in ECCV 2020
HIGHLIGHT:      Specifically, we proposed a novel semi-supervised crowd counting method which is built upon two innovative components: (1) a set of inter-related binary segmentation tasks are derived from the original density map regression task as the surrogate prediction target; (2) the surrogate target predictors are learned from both labeled and unlabeled data by utilizing a proposed self-training scheme which fully exploits the underlying constraints of these binary segmentation tasks.

135, TITLE:     HoughNet: Integrating near and long-range evidence for bottom-up object detection
http://arxiv.org/abs/2007.02355
AUTHORS:        Nermin Samet ; Samet Hicsonmez ; Emre Akbas
COMMENTS:       to appear in ECCV 2020
HIGHLIGHT:      This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method.

136, TITLE:     PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
http://arxiv.org/abs/2004.05682
AUTHORS:        Chenglin Yang ; Adam Kortylewski ; Cihang Xie ; Yinzhi Cao ; Alan Yuille
COMMENTS:       To appear in ECCV 2020
HIGHLIGHT:      PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning

137, TITLE:     Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization
http://arxiv.org/abs/2001.08680
AUTHORS:        Zijie Zhuang ; Longhui Wei ; Lingxi Xie ; Tianyu Zhang ; Hengheng Zhang ; Haozhe Wu ; Haizhou Ai ; Qi Tian
HIGHLIGHT:      Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization

138, TITLE:     Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
http://arxiv.org/abs/2006.09562
AUTHORS:        Federico Baldassarre ; Kevin Smith ; Josephine Sullivan ; Hossein Azizpour
COMMENTS:       Published at the European Conference on Computer Vision, ECCV 2020 (Poster)
HIGHLIGHT:      This paper introduces a novel weakly-supervised method for visual relationship detection that relies on minimal image-level predicate labels.

139, TITLE:     Benchmarking Differentially Private Residual Networks for Medical Imagery
http://arxiv.org/abs/2005.13099
AUTHORS:        Sahib Singh ; Harshvardhan Sikka ; Sasikanth Kotti ; Andrew Trask
COMMENTS:       5 Pages, 4 Figures
HIGHLIGHT:      In this paper we measure the effectiveness of $\epsilon$-Differential Privacy (DP) when applied to medical imaging.

140, TITLE:     Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control
http://arxiv.org/abs/2007.03433
AUTHORS:        Jin Guo
HIGHLIGHT:      In this thesis, I propose a family of fully decentralized deep multi-agent reinforcement learning (MARL) algorithms to achieve high, real-time performance in network-level traffic signal control.

141, TITLE:     Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers
http://arxiv.org/abs/2007.05619
AUTHORS:        Ondrej Kuzelka
COMMENTS:       This version: Fixed typos in Section 7 and in Appendix A
HIGHLIGHT:      In this paper we extend this result to the two-variable fragment with counting quantifiers.

142, TITLE:     Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video
http://arxiv.org/abs/1911.10967
AUTHORS:        Miao Liu ; Siyu Tang ; Yin Li ; James Rehg
HIGHLIGHT:      Motivated by this, we adopt intentional hand movement as a future representation and propose a novel deep network that jointly models and predicts the egocentric hand motion, interaction hotspots and future action.

143, TITLE:     Progressive Transformers for End-to-End Sign Language Production
http://arxiv.org/abs/2004.14874
AUTHORS:        Ben Saunders ; Necati Cihan Camgoz ; Richard Bowden
HIGHLIGHT:      In this paper, we propose Progressive Transformers, a novel architecture that can translate from discrete spoken language sentences to continuous 3D skeleton pose outputs representing sign language.

144, TITLE:     Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
http://arxiv.org/abs/2003.07119
AUTHORS:        Majed El Helou ; Ruofan Zhou ; Sabine Süsstrunk
HIGHLIGHT:      We present an analysis, in the frequency domain, of degradation-kernel overfitting in super-resolution and introduce a conditional learning perspective that extends to both super-resolution and denoising.

145, TITLE:     Attributional Robustness Training using Input-Gradient Spatial Alignment
http://arxiv.org/abs/1911.13073
AUTHORS:        Mayank Singh ; Nupur Kumari ; Puneet Mangla ; Abhishek Sinha ; Vineeth N Balasubramanian ; Balaji Krishnamurthy
COMMENTS:       ECCV 2020, Code at https://github.com/nupurkmr9/Attributional-Robustness
HIGHLIGHT:      In this work, we study the problem of attributional robustness (i.e. models having robust explanations) by showing an upper bound for attributional vulnerability in terms of spatial correlation between the input image and its explanation map.

146, TITLE:     Few-shot Action Recognition with Permutation-invariant Attention
http://arxiv.org/abs/2001.03905
AUTHORS:        Hongguang Zhang ; Li Zhang ; Xiaojuan Qi ; Hongdong Li ; Philip H. S. Torr ; Piotr Koniusz
COMMENTS:       ECCV2020 Spotlight
HIGHLIGHT:      Many few-shot learning models focus on recognising images.

147, TITLE:     Unified Image and Video Saliency Modeling
http://arxiv.org/abs/2003.05477
AUTHORS:        Richard Droste ; Jianbo Jiao ; J. Alison Noble
COMMENTS:       Accepted at the European Conference on Computer Vision (ECCV) 2020. R. Droste and J. Jiao contributed equally to this work
HIGHLIGHT:      To address this we propose four novel domain adaptation techniques - Domain-Adaptive Priors, Domain-Adaptive Fusion, Domain-Adaptive Smoothing and Bypass-RNN - in addition to an improved formulation of learned Gaussian priors.

148, TITLE:     Learning Visual Commonsense for Robust Scene Graph Generation
http://arxiv.org/abs/2006.09623
AUTHORS:        Alireza Zareian ; Zhecan Wang ; Haoxuan You ; Shih-Fu Chang
COMMENTS:       To be presented at ECCV 2020
HIGHLIGHT:      We propose the first method to acquire visual commonsense such as affordance and intuitive physics automatically from data, and use that to improve the robustness of scene understanding.

149, TITLE:     Suppress and Balance: A Simple Gated Network for Salient Object Detection
http://arxiv.org/abs/2007.08074
AUTHORS:        Xiaoqi Zhao ; Youwei Pang ; Lihe Zhang ; Huchuan Lu ; Lei Zhang
COMMENTS:       Accepted in ECCV2020(oral). Code: https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency
HIGHLIGHT:      In this work, we propose a simple gated network (GateNet) to solve both issues at once.

150, TITLE:     SECure: A Social and Environmental Certificate for AI Systems
http://arxiv.org/abs/2006.06217
AUTHORS:        Abhishek Gupta ; Camylle Lanteigne ; Sara Kingsley
COMMENTS:       Accepted for presentation at the Canadian Society for Ecological Economics 2020 Research Symposium, Tracing the Veins 2020, ICML 2020 Deploying and Monitoring Machine Learning Systems workshop
HIGHLIGHT:      This work proposes an ESG-inspired framework combining socio-technical measures to build eco-socially responsible AI systems.

151, TITLE:     Deformation-Aware 3D Model Embedding and Retrieval
http://arxiv.org/abs/2004.01228
AUTHORS:        Mikaela Angelina Uy ; Jingwei Huang ; Minhyuk Sung ; Tolga Birdal ; Leonidas Guibas
COMMENTS:       Accepted for publication at ECCV 2020. Project page under https://deformscan2cad.github.io
HIGHLIGHT:      Thus, to retrieve the best models for fitting, we propose a novel deep embedding approach that learns the asymmetric relationships by leveraging location-dependent egocentric distance fields. We introduce a new problem of retrieving 3D models that are deformable to a given query shape and present a novel deep deformation-aware embedding to solve this retrieval task.

152, TITLE:     Worst-case iteration bounds for log barrier methods for problems with nonconvex constraints
http://arxiv.org/abs/1807.00404
AUTHORS:        Oliver Hinder ; Yinyu Ye
COMMENTS:       Minor edits
HIGHLIGHT:      We provide an IPM that finds a $\mu$-approximate Fritz John point by solving $\mathcal{O}( \mu^{-7/4})$ trust-region subproblems.

153, TITLE:     VIFB: A Visible and Infrared Image Fusion Benchmark
http://arxiv.org/abs/2002.03322
AUTHORS:        Xingchen Zhang ; Ping Ye ; Gang Xiao
COMMENTS:       11 pages, 5 figures, 5 tables. Accepted to CVPRW2020. Compared to the CVPRW2020 version, this version corrects minor mistakes in Table 4 and the first paragraph of Section 4.2
HIGHLIGHT:      In this paper, after briefly reviewing recent advances of visible and infrared image fusion, we present a visible and infrared image fusion benchmark (VIFB) which consists of 21 image pairs, a code library of 20 fusion algorithms and 13 evaluation metrics.

154, TITLE:     Spectral-GANs for High-Resolution 3D Point-cloud Generation
http://arxiv.org/abs/1912.01800
AUTHORS:        Sameera Ramasinghe ; Salman Khan ; Nick Barnes ; Stephen Gould
COMMENTS:       1 page: Added affiliations
HIGHLIGHT:      In this paper, we develop a principled approach to synthesize 3D point-clouds using a spectral-domain Generative Adversarial Network (GAN).

155, TITLE:     Stabilizing the Lottery Ticket Hypothesis
http://arxiv.org/abs/1903.01611
AUTHORS:        Jonathan Frankle ; Gintare Karolina Dziugaite ; Daniel M. Roy ; Michael Carbin
COMMENTS:       This article has been subsumed by "Linear Mode Connectivity and the Lottery Ticket Hypothesis" (arXiv:1912.05671, ICML 2020). Please read/cite that article instead
HIGHLIGHT:      In this paper, we argue that these efforts have struggled on deeper networks because they have focused on pruning precisely at initialization.

156, TITLE:     Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking
http://arxiv.org/abs/2002.11669
AUTHORS:        Fanta Camara ; Nicola Bellotto ; Serhan Cosar ; Dimitris Nathanael ; Matthias Althoff ; Jingyuan Wu ; Johannes Ruenz ; André Dietrich ; Charles W. Fox
COMMENTS:       Accepted for publication in the IEEE Transactions on Intelligent Transportation Systems
HIGHLIGHT:      This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians.

157, TITLE:     Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
http://arxiv.org/abs/2003.08375
AUTHORS:        Amir Rahimi ; Amirreza Shaban ; Thalaiyasingam Ajanthan ; Richard Hartley ; Byron Boots
COMMENTS:       ECCV 2020. formerly "In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization"
HIGHLIGHT:      In this work, we argue that learning only an objectness function is a weak form of knowledge transfer and propose to learn a classwise pairwise similarity function that directly compares two input proposals as well.

158, TITLE:     Guessing State Tracking for Visual Dialogue
http://arxiv.org/abs/2002.10340
AUTHORS:        Wei Pang ; Xiaojie Wang
COMMENTS:       Accepted at ECCV 2020. The paper is about how the Guesser in the GuessWhat?! game guess. More details can be found at https://github.com/xubuvd/guesswhat
HIGHLIGHT:      This paper proposes a guessing state for the Guesser, and regards guess as a process with change of guessing state through a dialogue.

159, TITLE:     Fast Video Object Segmentation using the Global Context Module
http://arxiv.org/abs/2001.11243
AUTHORS:        Yu Li ; Zhuoran Shen ; Ying Shan
COMMENTS:       To appear at ECCV 2020
HIGHLIGHT:      We developed a real-time, high-quality semi-supervised video object segmentation algorithm.

160, TITLE:     Digital Social Contracts: A Foundation for an Egalitarian and Just Digital Society
http://arxiv.org/abs/2005.06261
AUTHORS:        Luca Cardelli ; Liav Orgad ; Gal Shahaf ; Ehud Shapiro ; Nimrod Talmon
HIGHLIGHT:      Here, we present a formal definition of a digital social contract as agents that communicate asynchronously via crypto-speech acts, where the output of each agent is the input of all the other agents.

161, TITLE:     Improving Optical Flow on a Pyramid Level
http://arxiv.org/abs/1912.10739
AUTHORS:        Markus Hofinger ; Samuel Rota Bulò ; Lorenzo Porzi ; Arno Knapitsch ; Thomas Pock ; Peter Kontschieder
HIGHLIGHT:      In this work we review the coarse-to-fine spatial feature pyramid concept, which is used in state-of-the-art optical flow estimation networks to make exploration of the pixel flow search space computationally tractable and efficient.

162, TITLE:     Unsupervised Quality Estimation for Neural Machine Translation
http://arxiv.org/abs/2005.10608
AUTHORS:        Marina Fomicheva ; Shuo Sun ; Lisa Yankovskaya ; Frédéric Blain ; Francisco Guzmán ; Mark Fishel ; Nikolaos Aletras ; Vishrav Chaudhary ; Lucia Specia
COMMENTS:       Accepted for publication in TACL. Authors' final version
HIGHLIGHT:      As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.

163, TITLE:     Building a COVID-19 Vulnerability Index
http://arxiv.org/abs/2003.07347
AUTHORS:        Dave DeCaprio ; Joseph Gartner ; Thadeus Burgess ; Kristian Garcia ; Sarthak Kothari ; Shaayan Sayed ; Carol J. McCall
HIGHLIGHT:      We present the results for three models predicting such complications, with each model increasing predictive effectiveness at the expense of ease of implementation.

164, TITLE:     Bridging Knowledge Graphs to Generate Scene Graphs
http://arxiv.org/abs/2001.02314
AUTHORS:        Alireza Zareian ; Svebor Karaman ; Shih-Fu Chang
COMMENTS:       To be presented at ECCV 2020
HIGHLIGHT:      In this paper, we present a unified formulation of these two constructs, where a scene graph is seen as an image-conditioned instantiation of a commonsense knowledge graph.

165, TITLE:     Deep Automodulators
http://arxiv.org/abs/1912.10321
AUTHORS:        Ari Heljakka ; Yuxin Hou ; Juho Kannala ; Arno Solin
COMMENTS:       24 pages, 16 figures
HIGHLIGHT:      We introduce a new category of generative autoencoders called automodulators.

166, TITLE:     Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation
http://arxiv.org/abs/2007.07077
AUTHORS:        Le Thanh Nguyen-Meidine ; Atif Bela ; Madhu Kiran ; Jose Dolz ; Louis-Antoine Blais-Morin ; Eric Granger
COMMENTS:       Submitted to WACV-2021
HIGHLIGHT:      In this paper, we propose a new unsupervised MTDA approach to train a common CNN that can generalize across multiple target domains.

167, TITLE:     Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes
http://arxiv.org/abs/2007.07743
AUTHORS:        Marcelo Gennari do Nascimento ; Theo W. Costain ; Victor Adrian Prisacariu
COMMENTS:       Accepted for publication at ECCV 2020. Code availiable at https://code.active.vision . Updated for typo
HIGHLIGHT:      We propose a novel method for neural network quantization that casts the neural architecture search problem as one of hyperparameter search to find non-uniform bit distributions throughout the layers of a CNN.

168, TITLE:     Broad Area Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections from Deep Neural Networks
http://arxiv.org/abs/2003.10566
AUTHORS:        Alan B. Cannaday II ; Curt H. Davis ; Grant J. Scott ; Blake Ruprecht ; Derek T. Anderson
COMMENTS:       9 pages, 9 figures, 9 tables, pre-published expansion of IGARSS2019 conference paper "Improved Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections from Deep Neural Networks"
HIGHLIGHT:      Here we demonstrate how Deep Neural Network (DNN) detections of multiple constitutive or component objects that are part of a larger, more complex, and encompassing feature can be spatially fused to improve the search, detection, and retrieval (ranking) of the larger complex feature.

169, TITLE:     Perceive, Transform, and Act: Multi-Modal Attention Networks for Vision-and-Language Navigation
http://arxiv.org/abs/1911.12377
AUTHORS:        Federico Landi ; Lorenzo Baraldi ; Marcella Cornia ; Massimiliano Corsini ; Rita Cucchiara
COMMENTS:       A revised version of this paper is currently under consideration at Computer Vision and Image Understanding
HIGHLIGHT:      In this paper, we strive for the creation of an agent able to tackle three key issues: multi-modality, long-term dependencies, and adaptability towards different locomotive settings.

170, TITLE:     Deep Hough Transform for Semantic Line Detection
http://arxiv.org/abs/2003.04676
AUTHORS:        Qi Han ; Kai Zhao ; Jun Xu ; Ming-Ming Cheng
COMMENTS:       https://github.com/Hanqer/deep-hough-transform
HIGHLIGHT:      In this paper, we put forward a simple yet effective method to detect meaningful straight lines, a.k.a. semantic lines, in given scenes.

171, TITLE:     The Group Loss for Deep Metric Learning
http://arxiv.org/abs/1912.00385
AUTHORS:        Ismail Elezi ; Sebastiano Vascon ; Alessandro Torcinovich ; Marcello Pelillo ; Laura Leal-Taixe
COMMENTS:       Accepted to European Conference on Computer Vision (ECCV) 2020, includes non-archival supplementary material
HIGHLIGHT:      We propose Group Loss, a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting, at the same time, low-density regions amongst data points belonging to different groups.

172, TITLE:     Stochastic Latent Residual Video Prediction
http://arxiv.org/abs/2002.09219
AUTHORS:        Jean-Yves Franceschi ; Edouard Delasalles ; Mickaël Chen ; Sylvain Lamprier ; Patrick Gallinari
HIGHLIGHT:      In this paper, we overcome these difficulties by introducing a novel stochastic temporal model whose dynamics are governed in a latent space by a residual update rule.

173, TITLE:     Orthogonal Over-Parameterized Training
http://arxiv.org/abs/2004.04690
AUTHORS:        Weiyang Liu ; Rongmei Lin ; Zhen Liu ; James M. Rehg ; Li Xiong ; Adrian Weller ; Le Song
COMMENTS:       Technical Report v3 (42 pages)
HIGHLIGHT:      We propose a novel orthogonal over-parameterized training (OPT) framework that can provably minimize the hyperspherical energy which characterizes the diversity of neurons on a hypersphere.

174, TITLE:     Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
http://arxiv.org/abs/2007.03357
AUTHORS:        Mobarakol Islam ; Lalithkumar Seenivasan ; Lim Chwee Ming ; Hongliang Ren
COMMENTS:       Accepted in MICCAI 2020
HIGHLIGHT:      For this purpose, we develop an approach to generate the scene graph and predict surgical interactions between instruments and surgical region of interest (ROI) during robot-assisted surgery.

175, TITLE:     It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
http://arxiv.org/abs/2004.02025
AUTHORS:        Karttikeya Mangalam ; Harshayu Girase ; Shreyas Agarwal ; Kuan-Hui Lee ; Ehsan Adeli ; Jitendra Malik ; Adrien Gaidon
COMMENTS:       Accepted at ECCV 2020 (Oral)
HIGHLIGHT:      In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.

176, TITLE:     On the Complexity of $λ_\infty\,,$ Vertex Expansion, and Spread Constant of Trees
http://arxiv.org/abs/2003.05582
AUTHORS:        Majid Farhadi ; Anand Louis ; Prasad Tetali
HIGHLIGHT:      For trees, we provide fast combinatorial algorithms that avoid solving a semidefinite relaxation of the problem.

177, TITLE:     SimAug: Learning Robust Representations from Simulation for Trajectory Prediction
http://arxiv.org/abs/2004.02022
AUTHORS:        Junwei Liang ; Lu Jiang ; Alexander Hauptmann
COMMENTS:       Accepted by ECCV 2020. Project website: https://next.cs.cmu.edu/simaug
HIGHLIGHT:      We propose a novel approach to learn robust representation through augmenting the simulation training data such that the representation can better generalize to unseen real-world test data.

178, TITLE:     Appearance Fusion of Multiple Cues for Video Co-localization
http://arxiv.org/abs/2003.09556
AUTHORS:        Koteswar Rao Jerripothula
COMMENTS:       17 Pages and 8 figures. Submitted to ACCV20
HIGHLIGHT:      In contrast to the usual spatial fusion approach, a novel appearance fusion approach is presented here.

179, TITLE:     Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification
http://arxiv.org/abs/2005.11960
AUTHORS:        Maxim Pisov ; Vladimir Kondratenko ; Alexey Zakharov ; Alexey Petraikin ; Victor Gombolevskiy ; Sergey Morozov ; Mikhail Belyaev
COMMENTS:       Accepted to MICCAI-2020
HIGHLIGHT:      We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then to simultaneously detect individual vertebrae and quantify fractures in 2D.

180, TITLE:     Scientific Discovery by Generating Counterfactuals using Image Translation
http://arxiv.org/abs/2007.05500
AUTHORS:        Arunachalam Narayanaswamy ; Subhashini Venugopalan ; Dale R. Webster ; Lily Peng ; Greg Corrado ; Paisan Ruamviboonsuk ; Pinal Bavishi ; Rory Sayres ; Abigail Huang ; Siva Balasubramanian ; Michael Brenner ; Philip Nelson ; Avinash V. Varadarajan
COMMENTS:       Accepted at MICCAI 2020. This version combines camera-ready and supplement
HIGHLIGHT:      We demonstrate that the proposed framework is able to explain the underlying scientific mechanism, thus bridging the gap between the model's performance and human understanding.

181, TITLE:     DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks
http://arxiv.org/abs/1910.11091
AUTHORS:        Li Xiao ; Chunlong Luo ; Tianqi Yu ; Yufan Luo ; Manqing Wang ; Fuhai Yu ; Yinhao Li ; Chan Tian ; Jie Qiao
HIGHLIGHT:      To automate the enumeration process, we develop a chromosome enumeration framework, DeepACEv2, based on the region based object detection scheme.

182, TITLE:     A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
http://arxiv.org/abs/2003.07743
AUTHORS:        Zequn Sun ; Qingheng Zhang ; Wei Hu ; Chengming Wang ; Muhao Chen ; Farahnaz Akrami ; Chengkai Li
COMMENTS:       Accepted in the 46th International Conference on Very Large Data Bases (VLDB 2020)
HIGHLIGHT:      In this paper, we conduct a comprehensive experimental study of this emerging field. We also propose a new KG sampling algorithm, with which we generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation.

183, TITLE:     Preparing Lessons: Improve Knowledge Distillation with Better Supervision
http://arxiv.org/abs/1911.07471
AUTHORS:        Tiancheng Wen ; Shenqi Lai ; Xueming Qian
HIGHLIGHT:      We introduce two novel approaches, Knowledge Adjustment (KA) and Dynamic Temperature Distillation (DTD), to penalize bad supervision and improve student model.

184, TITLE:     Machine Unlearning
http://arxiv.org/abs/1912.03817
AUTHORS:        Lucas Bourtoule ; Varun Chandrasekaran ; Christopher A. Choquette-Choo ; Hengrui Jia ; Adelin Travers ; Baiwu Zhang ; David Lie ; Nicolas Papernot
HIGHLIGHT:      We introduce SISA training, a framework that expedites the unlearning process by strategically limiting the influence of a data point in the training procedure.

185, TITLE:     A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks
http://arxiv.org/abs/2005.08636
AUTHORS:        Divyam Aggarwal ; Dhish Kumar Saxena ; Thomas Bäck ; Michael Emmerich
COMMENTS:       23 pages, 6 figures, 5 tables
HIGHLIGHT:      To bridge the research gap, this paper proposes a novel CG heuristic, which has enabled in-house development of an Airline Crew Pairing Optimizer (AirCROP).