Attention Guided CAM: Visual Explanations of Vision Transformer Guided by Self-Attention

• URL: http://arxiv.org/abs/2402.04563v1
• Date: Wed, 7 Feb 2024 03:43:56 GMT
• Title: Attention Guided CAM: Visual Explanations of Vision Transformer Guided by Self-Attention
• Authors: Saebom Leem, Hyunseok Seo
• Abstract summary: We propose an attention-guided visualization method applied to ViT that provides a high-level semantic explanation for its decision. Our method provides elaborate high-level semantic explanations with great localization performance only with the class labels.
• Score: 2.466595763108917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision Transformer(ViT) is one of the most widely used models in the computer vision field with its great performance on various tasks. In order to fully utilize the ViT-based architecture in various applications, proper visualization methods with a decent localization performance are necessary, but these methods employed in CNN-based models are still not available in ViT due to its unique structure. In this work, we propose an attention-guided visualization method applied to ViT that provides a high-level semantic explanation for its decision. Our method selectively aggregates the gradients directly propagated from the classification output to each self-attention, collecting the contribution of image features extracted from each location of the input image. These gradients are additionally guided by the normalized self-attention scores, which are the pairwise patch correlation scores. They are used to supplement the gradients on the patch-level context information efficiently detected by the self-attention mechanism. This approach of our method provides elaborate high-level semantic explanations with great localization performance only with the class labels. As a result, our method outperforms the previous leading explainability methods of ViT in the weakly-supervised localization task and presents great capability in capturing the full instances of the target class object. Meanwhile, our method provides a visualization that faithfully explains the model, which is demonstrated in the perturbation comparison test.

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