Summary of Maggie: Masked Guided Gradual Human Instance Matting, by Chuong Huynh et al.
MaGGIe: Masked Guided Gradual Human Instance Matting
by Chuong Huynh, Seoung Wug Oh, Abhinav Shrivastava, Joon-Young Lee
First submitted to arxiv on: 24 Apr 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 This paper proposes a new framework for human matting, called MaGGIe (Masked Guided Gradual Human Instance Matting), which predicts alpha mattes progressively for each human instance while maintaining computational efficiency and consistency. The method leverages transformer attention and sparse convolution to output all instance mattes simultaneously without increasing memory and latency costs. This approach achieves robust and versatile performance on synthesized benchmarks, introducing a novel multi-instance synthesis method that increases the generalization of models in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for computers to identify and extract human figures from images and videos. The new method, called MaGGIe, can do this for many people at once without slowing down or using too much memory. It’s like a superhero cape for machines that helps them understand what’s important in pictures and videos. |
Keywords
» Artificial intelligence » Attention » Generalization » Transformer
