Summary of Decomposing and Interpreting Image Representations Via Text in Vits Beyond Clip, by Sriram Balasubramanian et al.
Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP
by Sriram Balasubramanian, Samyadeep Basu, Soheil Feizi
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 new framework is introduced for interpreting arbitrary vision transformers (ViTs), such as CLIP-ViT, by decomposing their final representations into contributions from different model components. The framework automates the decomposition and linearly maps these contributions to the shared image-text representation space of CLIP, enabling interpretation via text. A novel scoring function is also proposed to rank components by their importance with respect to specific features. The approach is applied to various ViT variants, providing insights into the roles of different components concerning particular image features. These insights facilitate applications such as image retrieval using text descriptions or reference images, visualizing token importance heatmaps, and mitigating spurious correlations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a new way to understand how computer vision models work! A team of researchers created a special tool that can break down the final results of these models into smaller pieces, showing which parts are most important for recognizing certain features like shapes or colors. They tested this tool on different types of models and found that it helps us better understand what each part is doing. This new understanding can be used to improve things like searching for images based on text descriptions, creating maps of how important each word is in an image, and reducing mistakes caused by unrelated patterns. |
Keywords
» Artificial intelligence » Token » Vit
