Independent Block-Wise Attribution for Vision Transformer Interpretability through Semantic Relevance

Transformers are increasingly becoming the dominant model in the field of computer vision, thereby catalyzing research efforts aimed at unraveling the interpretability of transformers. Existing explanation techniques, whether attention-based or gradient-based, furnish a dependable approach to quantifying the impact of input features on model predictions from the perspective of dissecting self-attention mechanisms. However, current research overlooks the block-to-block constraints, which result in misdirection in attribution. In this work, we propose a block-wise constraints-free interpretation method, Independent Block Level Attribution (IBA), which maintains the relative independence of each block in the model. The IBA reconfigures the model into mutually unaffected class-semantic blocks via class-semantic relevance, each of which performs the attribution computation independently, thus minimizing the influence of inter-block constraints on the model interpretation performance. Extensive perturbation and segmentation experiments unequivocally demonstrate the superiority of our method, showcasing its significant outperformance compared to current interpretation methods. Additionally, we also apply IBA to the text transformer to demonstrate the generalization of our method.