Summary of Low-resolution Face Recognition Via Adaptable Instance-relation Distillation, by Ruixin Shi et al.
Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation
by Ruixin Shi, Weijia Guo, Shiming Ge
First submitted to arxiv on: 3 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 A recent paper proposes an adaptable instance-relation distillation approach for improving low-resolution face recognition by splitting the knowledge transfer process into distillation and adaptation steps. The method involves student models learning from high-resolution teacher models at both instance level and relation level, providing sufficient cross-resolution knowledge transfer. This allows the learned student model to be adaptable in recognizing low-resolution faces with adaptive batch normalization in inference. The approach effectively enhances the capability of recovering missing details of familiar low-resolution faces, leading to better knowledge transfer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low-resolution face recognition is a tough task because it’s hard for computers to tell faces apart when some parts are blurry or missing. Some researchers have tried using special training methods called “knowledge distillation” to help computers recognize low-res faces by giving them hints from high-res faces. But these methods often don’t work well because they’re based on assumptions that aren’t true in real-world situations. To fix this, the authors of a new paper divided the process into two steps: learning and adapting. They developed an approach called “instance-relation distillation” that helps computers learn to recognize low-res faces by giving them hints from high-res faces at both big-picture (instance) and detailed (relation) levels. This allows the computer to adapt to different situations and recover missing details in familiar low-res faces. |
Keywords
» Artificial intelligence » Batch normalization » Distillation » Face recognition » Inference » Knowledge distillation » Student model
