Summary of Dynamic Modality-camera Invariant Clustering For Unsupervised Visible-infrared Person Re-identification, by Yiming Yang et al.
Dynamic Modality-Camera Invariant Clustering for Unsupervised Visible-Infrared Person Re-identification
by Yiming Yang, Weipeng Hu, Haifeng Hu
First submitted to arxiv on: 11 Dec 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 Dynamic Modality-Camera Invariant Clustering (DMIC) framework for unsupervised learning visible-infrared person re-identification (USL-VI-ReID) tackles the limitations of existing clustering approaches. These methods often ignore cross-camera differences, leading to inaccurate and unreliable cross-modal associations. DMIC integrates three key components: Modality-Camera Invariant Expansion (MIE), Dynamic Neighborhood Clustering (DNC), and Hybrid Modality Contrastive Learning (HMCL). MIE bridges modalities and cameras at the clustering level by fusing inter-modal and inter-camera distance coding. DNC refines the network’s optimization objective using dynamic search strategies, transitioning from improving discriminability to enhancing cross-modal and cross-camera generalizability. HMCL optimizes instance-level and cluster-level distributions, updating memories for intra-modality and inter-modality training. Experimental results demonstrate that DMIC achieves competitive performance, significantly reducing the performance gap with supervised methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) is a way to group people in photos taken from different cameras or using different types of cameras. This makes it easier and cheaper than traditional methods that need lots of labeled data. Some methods do this already, but they don’t account for the differences between cameras, which can make mistakes. A new approach called Dynamic Modality-Camera Invariant Clustering (DMIC) tries to fix these problems by combining three ideas: making sure camera differences are ignored, using a special way to group people based on how similar they look, and learning from both within-camera and between-camera data. This works well and can even match the performance of methods that need labeled data. |
Keywords
» Artificial intelligence » Clustering » Optimization » Supervised » Unsupervised
