Summary of End-to-end Human Instance Matting, by Qinglin Liu et al.
End-to-End Human Instance Matting
by Qinglin Liu, Shengping Zhang, Quanling Meng, Bineng Zhong, Peiqiang Liu, Hongxun Yao
First submitted to arxiv on: 3 Mar 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 proposed End-to-End Human Instance Matting (E2E-HIM) framework is a novel approach to simultaneous multiple instance matting. It consists of three components: a general perception network, a united guidance network, and an instance matting network. The general perception network extracts image features and decodes instance contexts into latent codes. The united guidance network generates united semantics guidance that encodes the locations and semantic correspondences of all instances. Finally, the instance matting network predicts all instance-level alpha mattes. The framework is evaluated on a large-scale human instance matting dataset (HIM-100K) and outperforms existing methods with 50% lower errors and 5X faster speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to separate people in images. It’s called End-to-End Human Instance Matting, or E2E-HIM for short. The method uses three parts: one that looks at the whole image, one that focuses on each person, and one that puts it all together. This helps create more accurate and fast results when separating people in an image. |
Keywords
» Artificial intelligence » Semantics
