2019/04/30 by Masanari Kimura, Masayuki Tanaka, Kimura, Masanari +1 · 1 citation
Computer Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Human Pose and Action Recognition #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.1904.13078
CVPR2019 Workshop on Explainable AI
arxiv created 2019/04/30 · openalex publication_date 2019/04/30 · arxiv updated 2019/05/01 · openalex created_date 2019/05/09 · openalex updated_date 2026/07/28
Convolutional Neural Networks have achieved impressive results in various tasks, but interpreting the internal mechanism is a challenging problem. To tackle this problem, we exploit a multi-channel attention mechanism in feature space. Our network architecture allows us to obtain an attention mask for each feature while existing CNN visualization methods provide only a common attention mask for all features. We apply the proposed multi-channel attention mechanism to multi-attribute recognition task. We can obtain different attention mask for each feature and for each attribute. Those analyses give us deeper insight into the feature space of CNNs. The experimental results for the benchmark dataset show that the proposed method gives high interpretability to humans while accurately grasping the attributes of the data.