STAA: Spatio-Temporal Attention Attribution for Real-Time Interpreting Transformer-based Video Models

• URL: http://arxiv.org/abs/2411.00630v1
• Date: Fri, 01 Nov 2024 14:40:07 GMT
• Title: STAA: Spatio-Temporal Attention Attribution for Real-Time Interpreting Transformer-based Video Models
• Authors: Zerui Wang, Yan Liu,
• Abstract summary: Transformer-based models have achieved state-of-the-art performance in various computer vision tasks, including image and video analysis. Current Explainable AI (XAI) methods can only provide one-dimensional feature importance, either spatial or temporal explanation. This paper introduces STAA (Spatio-Temporal Attention Attribution), an XAI method for interpreting video Transformer models.
• Score: 7.500941533148728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based models have achieved state-of-the-art performance in various computer vision tasks, including image and video analysis. However, Transformer's complex architecture and black-box nature pose challenges for explainability, a crucial aspect for real-world applications and scientific inquiry. Current Explainable AI (XAI) methods can only provide one-dimensional feature importance, either spatial or temporal explanation, with significant computational complexity. This paper introduces STAA (Spatio-Temporal Attention Attribution), an XAI method for interpreting video Transformer models. Differ from traditional methods that separately apply image XAI techniques for spatial features or segment contribution analysis for temporal aspects, STAA offers both spatial and temporal information simultaneously from attention values in Transformers. The study utilizes the Kinetics-400 dataset, a benchmark collection of 400 human action classes used for action recognition research. We introduce metrics to quantify explanations. We also apply optimization to enhance STAA's raw output. By implementing dynamic thresholding and attention focusing mechanisms, we improve the signal-to-noise ratio in our explanations, resulting in more precise visualizations and better evaluation results. In terms of computational overhead, our method requires less than 3\% of the computational resources of traditional XAI methods, making it suitable for real-time video XAI analysis applications. STAA contributes to the growing field of XAI by offering a method for researchers and practitioners to analyze Transformer models.

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