Attention Guided CAM: Visual Explanations of Vision Transformer Guided
  by Self-Attention
Saebom Leem Hyunseok Seo
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.