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A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and…
PyTorch code for our paper "Grounding-IQA: Multimodal Language Grounding Model for Image Quality Assessment"
Official released code for VQA² series models
[CVPR 2025] Official Dataloader and Evaluation Scripts for VideoAutoArena.
[CVPR 2025] Official Dataloader and Evaluation Scripts for VideoAutoBench.
MathVista: data, code, and evaluation for Mathematical Reasoning in Visual Contexts
[ACL 2024 Findings] "TempCompass: Do Video LLMs Really Understand Videos?", Yuanxin Liu, Shicheng Li, Yi Liu, Yuxiang Wang, Shuhuai Ren, Lei Li, Sishuo Chen, Xu Sun, Lu Hou
EgoVid-5M: A Large-Scale Video-Action Dataset for Egocentric Video Generation
A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability
VideoNIAH: A Flexible Synthetic Method for Benchmarking Video MLLMs
🔥🔥MLVU: Multi-task Long Video Understanding Benchmark
PyTorch code for our paper "Dog-IQA: Standard-guided Zero-shot MLLM for Mix-grain Image Quality Assessment"
Codebase for Aria - an Open Multimodal Native MoE
[ICML'24 Spotlight] LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
[Neurips 24 Spotlight] Training in Pairs + Inference on Single Image with Anchors
Accelerating the development of large multimodal models (LMMs) with one-click evaluation module - lmms-eval.
Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks
🏆 [CVPRW 2024] COVER: A Comprehensive Video Quality Evaluator. 🥇 Winner solution for Video Quality Assessment Challenge at the 1st AIS 2024 workshop @ CVPR 2024
[Neurips 24' D&B] Official Dataloader and Evaluation Scripts for LongVideoBench.
[ICLR 2025] What do we expect from LMMs as AIGI evaluators and how do they perform?
Cornell NLVR and NLVR2 are natural language grounding datasets. Each example shows a visual input and a sentence describing it, and is annotated with the truth-value of the sentence.
An open-source implementation for training LLaVA-NeXT.
[NeurIPS 2024] This repo contains evaluation code for the paper "Are We on the Right Way for Evaluating Large Vision-Language Models"