Summary of Unsamflow: Unsupervised Optical Flow Guided by Segment Anything Model, By Shuai Yuan et al.
UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model
by Shuai Yuan, Lei Luo, Zhuo Hui, Can Pu, Xiaoyu Xiang, Rakesh Ranjan, Denis Demandolx
First submitted to arxiv on: 4 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 proposed UnSAMFlow network is an unsupervised optical flow approach that leverages object information from the Segment Anything Model (SAM) to improve the accuracy of traditional methods. The network includes a self-supervised semantic augmentation module tailored to SAM masks, a new smoothness definition based on homography, and a mask feature module for aggregating features on the object level. UnSAMFlow produces clear optical flow estimation with sharp boundaries around objects, outperforming state-of-the-art methods on KITTI and Sintel datasets, while also generalizing well across domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UnSAMFlow is a new way to estimate how things move in videos without using labels. It’s like having a superpower that helps machines understand what’s happening in videos. The approach uses information from the Segment Anything Model (SAM) to make it better than other methods. SAM helps the network know what objects are and where they are, which makes it good at estimating motion boundaries. This method is fast and works well on different types of data. |
Keywords
» Artificial intelligence » Mask » Optical flow » Sam » Self supervised » Unsupervised
