人工知能学会

Visual explanation methods, such as CAM and Grad-CAM, have been proposed to visualize and interpret the decision-making of CNNs. Recently, there are some other works that not only aim to provide better visual explanations, but also to improve the performance of CNNs by using visual explanations. In this work, we propose a network architecture — MANet that generates visual explanation during the inference process using a mixed attention module for adaptive feature refinement and also uses the generated attention map to improve network performance on image recognition tasks. Experimental results show that our proposed MANet achieves better visual performance and outperforms the baseline models on both image classification and object detection tasks.