Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization-Reference-Cited by-同舟云学术

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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Published:2019-10-11 Issue:2 Volume:128 Page:336-359
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Selvaraju Ramprasaath R.,Cogswell Michael,Das Abhishek,Vedantam Ramakrishna,Parikh Devi,Batra Dhruv

Funder

National Science Foundation

Defense Advanced Research Projects Agency

Office of Naval Research

Alfred P. Sloan Foundation

Army Research Office

Paul G. Allen Family Foundation

Google

Amazon Web Services

Nvidia

Virginia Polytechnic Institute and State University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s11263-019-01228-7.pdf

Reference62 articles.

1. Agrawal, A., Batra, D., & Parikh, D. (2016). Analyzing the behavior of visual question answering models. In EMNLP.

2. Agrawal, H., Mathialagan, C. S., Goyal, Y., Chavali, N., Banik, P., Mohapatra, A., Osman, A., & Batra, D. (2015). CloudCV: Large scale distributed computer vision as a cloud service. In Mobile cloud visual media computing (pp. 265–290). Springer.

3. Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C., & Parikh, D. (2015). VQA: Visual question answering. In ICCV.

4. Bau, D., Zhou, B., Khosla, A., Oliva, A., & Torralba, A. (2017). Network dissection: Quantifying interpretability of deep visual representations. In computer vision and pattern recognition.

5. Bazzani, L., Bergamo, A., Anguelov, D., Torresani, L. (2016). Self-taught object localization with deep networks. In WACV.

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