Summary of Identifying Important Group Of Pixels Using Interactions, by Kosuke Sumiyasu et al.
Identifying Important Group of Pixels using Interactions
by Kosuke Sumiyasu, Kazuhiko Kawamoto, Hiroshi Kera
First submitted to arxiv on: 8 Jan 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 proposed MoXI method efficiently identifies groups of pixels with high prediction confidence in image classifiers. By employing game-theoretic concepts and interactions, MoXI considers the effects of individual pixels and cooperative influences on model confidence. This approach outperforms existing visualization methods like Grad-CAM, Attention rollout, and Shapley value. Theoretical analysis and experiments demonstrate the effectiveness of MoXI, which reduces the computational cost to quadratic complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image classifiers are used to analyze images, but we don’t fully understand how they make predictions. To help with this, a new method called MoXI was created. It looks at individual pixels in an image and sees if they’re important for the model’s prediction. MoXI is better than other methods because it considers how all the pixels work together to make the prediction. |
Keywords
* Artificial intelligence * Attention
