Summary of Statistical Test For Attention Map in Vision Transformer, by Tomohiro Shiraishi et al.
Statistical Test for Attention Map in Vision Transformer
by Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka, Vo Nguyen Le Duy, Kouichi Taji, Ichiro Takeuchi
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 Vision Transformer (ViT) has shown impressive performance in various computer vision tasks, leveraging attention mechanisms to capture complex relationships among image patches. However, when using ViT’s attentions as evidence in high-stakes decision-making tasks like medical diagnostics, a challenge arises due to the potential for irrelevant region focus. To address this, we propose a statistical test for ViT’s attentions, enabling reliable quantitative indicators with rigorous error control. We demonstrate the effectiveness of our method through numerical experiments and brain image diagnoses applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Vision Transformer is a powerful tool in computer vision that helps machines understand images better. It uses something called attention to figure out what parts of an image are most important. But when we use this power for important decisions, like diagnosing medical conditions, we need to make sure the model isn’t focusing on the wrong things. To do this, we created a test that shows us how reliable these attention signals are. We tested it and found it works well, even when applied to real-life brain image diagnoses. |
Keywords
* Artificial intelligence * Attention * Vision transformer * Vit
