Summary of Look One and More: Distilling Hybrid Order Relational Knowledge For Cross-resolution Image Recognition, by Shiming Ge and Kangkai Zhang and Haolin Liu and Yingying Hua and Shengwei Zhao and Xin Jin and Hao Wen
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition
by Shiming Ge, Kangkai Zhang, Haolin Liu, Yingying Hua, Shengwei Zhao, Xin Jin, Hao Wen
First submitted to arxiv on: 9 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 teacher-student learning approach for recognizing low-resolution images by leveraging high-resolution images. Recent deep models have achieved great success in image recognition tasks, but directly applying them to low-resolution images may result in poor accuracy due to the loss of informative details during resolution degradation. The proposed approach consists of three streams: a teacher stream pretrained to recognize high-resolution images with high accuracy, a student stream learned to identify low-resolution images by mimicking the teacher’s behaviors, and an extra assistant stream introduced as a bridge to facilitate knowledge transfer. The learning of the student is supervised with multiple losses that preserve similarities in various order relational structures. This approach enables effective enhancement of recovering missing details of familiar low-resolution images, leading to better knowledge transfer. The paper demonstrates the effectiveness of this approach through extensive experiments on metric learning, low-resolution image classification, and low-resolution face recognition tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a way to make computers better at recognizing images that are blurry or not very clear. Even though computers are really good at recognizing clear pictures, they’re not as good when the picture is blurry. But people who are familiar with the clear image can still recognize it, even if it’s blurry! The researchers came up with an idea called “teacher-student learning” to help computers recognize blurry images better. They use three parts: one that learns from clear pictures, one that tries to recognize blurry pictures, and another part that helps them learn from each other. This makes the computer better at recognizing things it’s seen before when they’re not very clear. |
Keywords
» Artificial intelligence » Face recognition » Image classification » Supervised
