A Generic Visualization Approach for Convolutional Neural Networks

• URL: http://arxiv.org/abs/2007.09748v1
• Date: Sun, 19 Jul 2020 18:46:56 GMT
• Title: A Generic Visualization Approach for Convolutional Neural Networks
• Authors: Ahmed Taha, Xitong Yang, Abhinav Shrivastava, and Larry Davis
• Abstract summary: We formulate attention visualization as a constrained optimization problem. We leverage the unit L2-Norm constraint as an attention filter (L2-CAF) to localize attention in both classification and retrieval networks.
• Score: 48.30883603606862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval networks are essential for searching and indexing. Compared to classification networks, attention visualization for retrieval networks is hardly studied. We formulate attention visualization as a constrained optimization problem. We leverage the unit L2-Norm constraint as an attention filter (L2-CAF) to localize attention in both classification and retrieval networks. Unlike recent literature, our approach requires neither architectural changes nor fine-tuning. Thus, a pre-trained network's performance is never undermined L2-CAF is quantitatively evaluated using weakly supervised object localization. State-of-the-art results are achieved on classification networks. For retrieval networks, significant improvement margins are achieved over a Grad-CAM baseline. Qualitative evaluation demonstrates how the L2-CAF visualizes attention per frame for a recurrent retrieval network. Further ablation studies highlight the computational cost of our approach and compare L2-CAF with other feasible alternatives. Code available at https://bit.ly/3iDBLFv

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