Summary of Don’t Miss the Forest For the Trees: Attentional Vision Calibration For Large Vision Language Models, by Sangmin Woo et al.
Don’t Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
by Sangmin Woo, Donguk Kim, Jaehyuk Jang, Yubin Choi, Changick Kim
First submitted to arxiv on: 28 May 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 study tackles the issue with Large Vision Language Models (LVLMs) that often produce hallucinatory responses when asked to understand fine-grained visual object details. Researchers found that tokens receiving less attention can hold crucial information about object attributes and relationships, which are essential for accurate task performance. To address this problem, they propose a technique called Attentional Vision Calibration (AVC), which identifies blind tokens during the decoding phase and adjusts token predictions accordingly. This helps to balance the consideration of all tokens, reducing reliance on potentially misleading blind tokens. The study validates AVC’s effectiveness on benchmarks such as POPE, MME, and AMBER, showing improved performance over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision Language Models can sometimes get confused when asked about small details in images. This happens because they focus too much on a few important parts of the picture, missing out on other important information. Researchers looked at how to improve this by paying more attention to less important tokens and ignoring ones that don’t add much. They developed a way to do this called Attentional Vision Calibration (AVC). AVC helps LVLMs give better answers by making them consider all the relevant information in an image, not just a few key parts. |
Keywords
* Artificial intelligence * Attention * Token
