A Generic Visualization Approach for Convolutional Neural Networks

ECCV 2020

Ahmed Taha      Xitong Yang      Abhinav Shrivastava      Larry Davis

University Of Maryland College Park


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Overview Video




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.



Bibtex

@inproceedings{taha2020generic,
title={A Generic Visualization Approach for Convolutional Neural Networks},
author={Taha, Ahmed and Yang, Xitong and Shrivastava, Abhinav and Davis, Larry},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
year={2020}
}
									

Acknowledgements

This work was partially funded by independent grantsfrom Office of Naval Research (N000141612713) and Facebook AI