Summary of Camera-invariant Meta-learning Network For Single-camera-training Person Re-identification, by Jiangbo Pei et al.
Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification
by Jiangbo Pei, Zhuqing Jiang, Aidong Men, Haiying Wang, Haiyong Luo, Shiping Wen
First submitted to arxiv on: 21 Jun 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 proposed Camera-Invariant Meta-Learning Network (CIMN) tackles single-camera-training person re-identification (SCT re-ID), where a model is trained using SCT datasets with no cross-camera same-person data as supervision. Previous methods rely on assumptions that may not hold true, whereas CIMN assumes camera-invariant feature representations should be robust to camera changes. To achieve this, CIMN splits the training data into meta-train and meta-test sets based on camera IDs and employs a meta-learning strategy with three losses: meta triplet loss, meta classification loss, and meta camera alignment loss. This approach enables learning of camera-invariant and identity-discriminative representations without CCSP data, outperforming state-of-the-art methods on SCT re-ID benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CIMN is a new way to identify people in different cameras using just one picture of each person. It’s like recognizing a friend from a far-off distance even if you haven’t seen them for years. The problem is that there are no pictures of the same person taken by multiple cameras, which makes it hard for computers to learn how to do this task well. CIMN solves this problem by pretending that it has these extra pictures and using them to train its model. It also adds three new ways to measure how good the model is doing, which helps it improve even more. |
Keywords
» Artificial intelligence » Alignment » Classification » Meta learning » Triplet loss
