T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers

The paper introduces T-TAME, a novel trainable attention mechanism compatible with Vision Transformers and convolutional neural networks, designed to generate high-quality explanation maps for image classification models efficiently in a single forward pass. Applied to architectures like VGG-16, ResNet-50, and ViT-B-16 on ImageNet, T-TAME outperforms existing explainability methods, enhancing interpretability without the computational cost of perturbation-based techniques.