Summary of Remix: Training Generalized Person Re-identification on a Mixture Of Data, by Timur Mamedov et al.
ReMix: Training Generalized Person Re-identification on a Mixture of Data
by Timur Mamedov, Anton Konushin, Vadim Konushin
First submitted to arxiv on: 29 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 The proposed ReMix method addresses the limitations of modern person re-identification (Re-ID) by jointly training on a mixture of limited labeled multi-camera and large unlabeled single-camera data. The approach leverages novel data sampling strategies and loss functions to effectively train the model, achieving high generalization ability and outperforming state-of-the-art methods in generalizable person Re-ID. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new method called ReMix helps people be recognized correctly even when cameras change. This is important because most current methods don’t work well in different environments. The problem is that there’s not enough data to train these models, and what data does exist is limited. However, there are lots of pictures taken with single cameras that aren’t labeled. These can be used for training, but existing methods only use them briefly before focusing on the limited multi-camera data. ReMix changes this by using both types of data together, allowing it to learn more and work better in different environments. |
Keywords
» Artificial intelligence » Generalization
