Summary of Collaboration Of Teachers For Semi-supervised Object Detection, by Liyu Chen et al.
Collaboration of Teachers for Semi-supervised Object Detection
by Liyu Chen, Huaao Tang, Yi Wen, Hanting Chen, Wei Li, Junchao Liu, Jie Hu
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new framework for semi-supervised object detection (SSOD) that addresses limitations in current Consistency Regularization-based approaches. Specifically, it tackles weight coupling between teacher and student models, which reduces the utilization of unlabeled data and perpetuates confirmation bias on low-quality pseudo-labels. The Collaboration of Teachers Framework (CTF) introduces multiple pairs of teacher-student models, with a Data Performance Consistency Optimization module that selects the most reliable pseudo-labels generated by top-performing teachers to guide other students. This approach leads to improved utilization of unlabeled data and faster convergence. Experiments on various SSOD datasets show significant improvements in mean average precision (mAP) compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way for machines to learn from both labeled and unlabeled pictures. It helps them detect objects better by giving them more information, called pseudo-labels, which are like guesses about what’s in the picture. These guesses can be wrong, but this new method makes sure they’re not too bad by choosing the best ones and using them to teach other machines. This approach works really well on many different datasets and helps machines detect objects faster and more accurately. |
Keywords
» Artificial intelligence » Mean average precision » Object detection » Optimization » Regularization » Semi supervised
