Summary of Rethink Predicting the Optical Flow with the Kinetics Perspective, by Yuhao Cheng et al.
Rethink Predicting the Optical Flow with the Kinetics Perspective
by Yuhao Cheng, Siru Zhang, Yiqiang Yan
First submitted to arxiv on: 21 May 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 novel approach to optical flow estimation by combining apparent and kinetic information from consecutive frames. This method directly predicts optical flow from extracted image features instead of building a correlation volume, improving efficiency. A differentiable warp operation is introduced that considers both warping and occlusion. The approach also incorporates a self-supervised loss function that blends kinetic and apparent features. Experimental results show the proposed method outperforms state-of-the-art methods in certain metrics, particularly in situations with occlusion or fast motion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Optical flow estimation is a fundamental task in computer vision that helps describe how pixels move between frames. Traditionally, this involves building a correlation volume, but this can be slow and inaccurate when dealing with occlusion. The new approach combines information from consecutive frames to predict optical flow directly from image features. This makes the method faster and more accurate. It also includes a special operation for warping and occlusion. Tests show that this new way of thinking about optical flow performs better than previous methods. |
Keywords
» Artificial intelligence » Loss function » Optical flow » Self supervised
