Summary of Effective Guidance For Model Attention with Simple Yes-no Annotations, by Seongmin Lee et al.
Effective Guidance for Model Attention with Simple Yes-no Annotations
by Seongmin Lee, Ali Payani, Duen Horng Chau
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 method, CRAYON (Correcting Reasoning with Annotations of Yes Or No), tackles the issue of deep learning models focusing on irrelevant areas, leading to biased performance and limited generalization. By leveraging simple yes-no annotations, CRAYON offers effective, scalable, and practical solutions to rectify model attention. The approach empowers classical and modern model interpretation techniques, allowing for identifying and guiding model reasoning. Specifically, CRAYON-ATTENTION directs classic interpretations based on saliency maps to focus on relevant image regions, while CRAYON-PRUNING removes irrelevant neurons identified by modern concept-based methods to mitigate their influence. Experimental results demonstrate the effectiveness, scalability, and practicality of CRAYON in refining model attention, achieving state-of-the-art performance on three benchmark datasets and outperforming 12 methods that require more complex annotations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models can be biased because they focus too much on certain parts of an image. To fix this problem, researchers created a new method called CRAYON. CRAYON uses simple “yes” or “no” answers to help the model pay attention to the right things. This makes it easier to understand how the model is thinking and why it’s making certain predictions. CRAYON can even remove parts of the model that aren’t helping, which makes it more accurate. In tests, CRAYON did better than 12 other methods on three different image datasets. |
Keywords
» Artificial intelligence » Attention » Deep learning » Generalization » Pruning
