Summary of Self-supervised Keypoint Detection with Distilled Depth Keypoint Representation, by Aman Anand et al.
Self-Supervised Keypoint Detection with Distilled Depth Keypoint Representation
by Aman Anand, Elyas Rashno, Amir Eskandari, Farhana Zulkernine
First submitted to arxiv on: 4 Oct 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 paper proposes a novel framework, Distill-DKP, for unsupervised keypoint detection that leverages depth maps and RGB images. Existing methods apply artificial deformations to images but lack depth information and often detect keypoints on the background. Distill-DKP uses cross-modal knowledge distillation to extract embedding-level knowledge from a depth-based teacher model and guide an image-based student model. The framework significantly outperforms previous unsupervised methods, reducing mean L2 error by 47.15% on Human3.6M, mean average error by 5.67% on Taichi, and improving keypoints accuracy by 1.3% on DeepFashion dataset. Detailed ablation studies demonstrate the sensitivity of knowledge distillation across different layers of the network. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find specific points (keypoints) in pictures without any help or labels. Existing methods try to make the image look distorted, but this isn’t very good because it doesn’t use information from other types of images (like depth maps). The researchers propose a new method called Distill-DKP that uses both RGB and depth map images together to find keypoints better than before. This helps reduce mistakes by 47% on one dataset, 6% on another, and improves accuracy by 1.3% on a third. The study shows how important it is to use knowledge from different parts of the image. |
Keywords
» Artificial intelligence » Embedding » Knowledge distillation » Student model » Teacher model » Unsupervised
