Summary of Towards Interpreting Visual Information Processing in Vision-language Models, by Clement Neo et al.
Towards Interpreting Visual Information Processing in Vision-Language Models
by Clement Neo, Luke Ong, Philip Torr, Mor Geva, David Krueger, Fazl Barez
First submitted to arxiv on: 9 Oct 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 The paper investigates the processing of visual tokens in prominent Vision-Language Models (VLMs) like LLaVA. It examines how object information is localized, how visual token representations evolve across layers, and how visual information is integrated for predictions. The study reveals that removing object-specific tokens reduces object identification accuracy by over 70%. Moreover, it shows that visual token representations become increasingly interpretable in the vocabulary space across layers, mirroring the process of textual tokens corresponding to image content. The model extracts object information from refined representations at the last token position for prediction, similar to text-only language models for factual association tasks. This research provides crucial insights into VLMs’ processing and integration of visual information, bridging the gap between language and vision models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how powerful Vision-Language Models (VLMs) process and understand text and images. It looks at how VLMs recognize objects in pictures and how this affects their predictions. The study shows that when VLMs don’t have special tokens for objects, they can’t identify those objects as well. It also finds that the way VLMs represent visual information gets better and more understandable as it goes through different layers. Finally, the model uses the refined object information to make its final prediction, similar to how language models work with text. |
Keywords
» Artificial intelligence » Token
