Summary of Efficient Low-resolution Face Recognition Via Bridge Distillation, by Shiming Ge and Shengwei Zhao and Chenyu Li and Yu Zhang and Jia Li
Efficient Low-Resolution Face Recognition via Bridge Distillation
by Shiming Ge, Shengwei Zhao, Chenyu Li, Yu Zhang, Jia Li
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes a bridge distillation approach to transform a complex face model trained on high-resolution faces into a light-weight model for low-resolution face recognition. The approach consists of two steps: cross-dataset distillation to transfer knowledge from private high-resolution faces to public high-resolution faces, and resolution-adapted distillation to learn low-resolution face representations. This results in a compact and efficient student model with impressive accuracy in recognizing low-resolution faces using only 0.21M parameters and 0.057MB memory. The approach also achieves fast inference speeds on various devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to recognize faces, even when they’re not high-quality images. They took a big model that was trained on lots of high-resolution face pictures and shrunk it down to be smaller and faster. This smaller model can still recognize low-resolution face pictures with good accuracy. The researchers used a special technique called distillation to transfer the knowledge from the big model to the small one. This new approach is useful because it can run quickly on devices like smartphones. |
Keywords
» Artificial intelligence » Distillation » Face recognition » Inference » Student model
