Summary of Learning to Balance: Diverse Normalization For Cloth-changing Person Re-identification, by Hongjun Wang et al.
Learning to Balance: Diverse Normalization for Cloth-Changing Person Re-Identification
by Hongjun Wang, Jiyuan Chen, Zhengwei Yin, Xuan Song, Yinqiang Zheng
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper addresses Cloth-Changing Person Re-Identification (CC-ReID), which involves recognizing individuals in images regardless of clothing status. Contrary to existing work that relies on clothing labels, silhouettes, or auxiliary data to balance clothing and identity features, this study finds that achieving this balance is challenging and nuanced. The proposed Diverse Norm module expands personal features into orthogonal spaces using channel attention to separate clothing and identity features. A sample re-weighting optimization strategy ensures the opposite optimization direction. The Diverse Norm approach outperforms state-of-the-art methods when integrated with ResNet50, making it a simple yet effective solution that does not require additional data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about recognizing people in pictures, even if they’re wearing different clothes. Most other studies try to balance what’s seen and what’s hidden under their clothes. But this study shows that trying to do both at the same time is actually harder than it seems. The new idea proposed in this paper helps separate what’s important (the person) from what’s not (their clothes). This makes it easier to recognize people, even if they’re wearing different outfits. It’s a simple and effective way to do this without needing extra information. |
Keywords
* Artificial intelligence * Attention * Optimization
