Summary of Staa: Spatio-temporal Attention Attribution For Real-time Interpreting Transformer-based Video Models, by Zerui Wang and Yan Liu
STAA: Spatio-Temporal Attention Attribution for Real-Time Interpreting Transformer-based Video Models
by Zerui Wang, Yan Liu
First submitted to arxiv on: 1 Nov 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 paper introduces STAA (Spatio-Temporal Attention Attribution), an Explainable AI (XAI) method that interprets video Transformer models, providing both spatial and temporal information simultaneously from attention values. Unlike traditional methods, which separately apply image XAI techniques for spatial features or segment contribution analysis for temporal aspects, STAA offers a holistic explanation of the model’s behavior. The study uses the Kinetics-400 dataset, a benchmark collection of 400 human action classes used for action recognition research, and introduces metrics to quantify explanations. To improve the signal-to-noise ratio in our explanations, we implement dynamic thresholding and attention focusing mechanisms, resulting in more precise visualizations and better evaluation results. Our method requires less than 3% of the computational resources of traditional XAI methods, making it suitable for real-time video XAI analysis applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand how video Transformer models work. These models are really good at recognizing actions in videos, but they’re hard to explain why they make certain predictions. The new method, called STAA, can show both where and when the model is paying attention in a video. This helps us understand how the model is working and makes it more useful for real-world applications. The researchers tested their method on a big dataset of videos with different actions and found that it was really good at explaining why the model made certain predictions. |
Keywords
» Artificial intelligence » Attention » Transformer
