Summary of Low-resolution Object Recognition with Cross-resolution Relational Contrastive Distillation, by Kangkai Zhang et al.
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation
by Kangkai Zhang, Shiming Ge, Ruixin Shi, Dan Zeng
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 cross-resolution relational contrastive distillation approach enables a low-resolution student model to mimic a well-trained teacher model, achieving high accuracy in identifying high-resolution objects. This knowledge transfer method utilizes contrastive relational distillation loss to preserve similarities in various relational structures, enhancing the capability of recovering missing details and improving familiar object recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to improve low-resolution object recognition by transferring knowledge from a high-resolution teacher model to a low-resolution student model. The approach uses cross-resolution relational contrastive distillation to align representations and preserve similarities. This allows for better recovery of missing details, leading to more accurate object recognition. Experiments on low-resolution object classification and face recognition demonstrate the effectiveness and adaptability of this method. |
Keywords
» Artificial intelligence » Classification » Distillation » Face recognition » Student model » Teacher model
