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DmitryRyumin authored Jan 26, 2025
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## 1201-1400 papers

![Section Papers](https://img.shields.io/badge/Section%20Papers-110-42BA16) ![Preprint Papers](https://img.shields.io/badge/Preprint%20Papers-75-b31b1b) ![Papers with Open Code](https://img.shields.io/badge/Papers%20with%20Open%20Code-47-1D7FBF) ![Papers with Video](https://img.shields.io/badge/Papers%20with%20Video-1-FF0000)
![Section Papers](https://img.shields.io/badge/Section%20Papers-110-42BA16) ![Preprint Papers](https://img.shields.io/badge/Preprint%20Papers-74-b31b1b) ![Papers with Open Code](https://img.shields.io/badge/Papers%20with%20Open%20Code-47-1D7FBF) ![Papers with Video](https://img.shields.io/badge/Papers%20with%20Video-1-FF0000)

| :id: | **Title** | **Repo** | **Paper** | **Video** |
|------|-----------|:--------:|:---------:|:---------:|
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| 5280 | Less is more: Label Recommendation for Weakly Supervised Point Cloud Semantic Segmentation | | [![arXiv](https://img.shields.io/badge/arXiv-2212.04744-b31b1b.svg)](https://arxiv.org/abs/2212.04744) | |
| 10719 | SAUI: Scale-Aware Unseen Imagineer for Zero-Shot Object Detection | | | |
| 4651 | Deciphering Raw Data in Neuro-Symbolic Learning with Provable Guarantees | | [![arXiv](https://img.shields.io/badge/arXiv-2001.07636-b31b1b.svg)](https://arxiv.org/abs/2001.07636) | |
| 5888 | What Effects the Generalization in Visual Reinforcement Learning: Policy Consistency with Truncated Return Prediction | | [![arXiv](https://img.shields.io/badge/arXiv-2212.08860-b31b1b.svg)](https://arxiv.org/abs/2212.08860) | |
| 5888 | What Effects the Generalization in Visual Reinforcement Learning: Policy Consistency with Truncated Return Prediction | | | |
| 6299 | Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment | | [![arXiv](https://img.shields.io/badge/arXiv-2209.07526-b31b1b.svg)](https://arxiv.org/abs/2209.07526) | |
| 4862 | FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants | | | |
| 12942 | COMBAT: Alternated Training for Effective Clean-Label Backdoor Attacks | | | |
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