Summary of Not All Diffusion Model Activations Have Been Evaluated As Discriminative Features, by Benyuan Meng et al.
Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features
by Benyuan Meng, Qianqian Xu, Zitai Wang, Xiaochun Cao, Qingming Huang
First submitted to arxiv on: 4 Oct 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 A recent study in diffusion models reveals that activations, initially designed for image generation, can also serve as dense features for various discriminative tasks like semantic segmentation. However, selecting a small yet effective subset of these numerous activations poses a fundamental problem. This paper takes a further step by evaluating a broader range of activations, including queries and keys used to compute attention scores, and those within embedded ViT modules. A full-scale quantitative comparison is no longer operational due to the significant increase in activations. Instead, this study aims to understand the properties of these activations, enabling filtering out inferior ones via simple qualitative evaluation. The research discovers three universal properties among diffusion models, leading to effective feature selection solutions for several popular diffusion models. Experimental results across multiple discriminative tasks validate the superiority of this method over SOTA competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion models are used for image generation, but researchers found that the “activations” inside these models can also help with other tasks like separating objects in an image. The problem is figuring out which of these many activations are most important. This paper looks at a lot more types of activations than previous studies and finds some patterns that help them pick the best ones. They tested their method on several different tasks and showed it works better than others. |
Keywords
» Artificial intelligence » Attention » Diffusion » Feature selection » Image generation » Semantic segmentation » Vit
