Summary of Diffusion Features to Bridge Domain Gap For Semantic Segmentation, by Yuxiang Ji et al.
Diffusion Features to Bridge Domain Gap for Semantic Segmentation
by Yuxiang Ji, Boyong He, Chenyuan Qu, Zhuoyue Tan, Chuan Qin, Liaoni Wu
First submitted to arxiv on: 2 Jun 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 In this study, researchers investigate how to utilize pre-trained diffusion models for cross-domain semantic segmentation tasks. The team proposes a new approach called DIffusion Feature Fusion (DIFF), which leverages sampling and fusion techniques to extract effective semantic representations from the diffusion process. By introducing a novel training framework that learns posterior knowledge from text-to-image generation, the authors demonstrate that their method outperforms previous approaches in domain generalization semantic segmentation tasks, achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how pre-trained diffusion models can be used for image synthesis and cross-domain semantic segmentation. The researchers develop a new approach called DIFF, which uses sampling and fusion techniques to extract useful information from the diffusion process. By learning from text-to-image generation, they show that their method is better at handling different domains than previous approaches. |
Keywords
» Artificial intelligence » Diffusion » Domain generalization » Image generation » Image synthesis » Semantic segmentation
