Summary of Attention Guided Cam: Visual Explanations Of Vision Transformer Guided by Self-attention, By Saebom Leem et al.
Attention Guided CAM: Visual Explanations of Vision Transformer Guided by Self-Attention
by Saebom Leem, Hyunseok Seo
First submitted to arxiv on: 7 Feb 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 The Vision Transformer (ViT) has revolutionized computer vision tasks with its impressive performance on various applications. However, the lack of proper visualization methods for ViT-based architectures hinders their full utilization. This paper proposes an attention-guided visualization method specifically designed for ViT, providing high-level semantic explanations for its decisions. The approach aggregates gradients propagated from the classification output to each self-attention location, guided by normalized self-attention scores. This supplements patch-level context information efficiently detected by the self-attention mechanism. Our method outperforms leading explainability methods in weakly-supervised localization tasks and captures full instances of target class objects. Moreover, it provides faithful visualizations that explain model decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Vision Transformer (ViT) is a powerful tool for computer vision tasks. But to really understand how it works, we need better ways to visualize its decision-making process. This paper shows how to do just that by creating a special method that explains ViT’s choices at a high level. It uses the unique structure of ViT to collect information from different parts of an image and then combines it with the attention scores to provide a detailed explanation of what the model is doing. The result is a way to visualize ViT that accurately shows how it makes decisions, which is important for using this technology in real-world applications. |
Keywords
» Artificial intelligence » Attention » Classification » Self attention » Supervised » Vision transformer » Vit
