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Informative Class Activation Maps

2021-06-19 11:02:59
Zhenyue Qin, Dongwoo Kim, Tom Gedeon
arXiv_CV
         
Abstract

We study how to evaluate the quantitative information content of a region within an image for a particular label. To this end, we bridge class activation maps with information theory. We develop an informative class activation map (infoCAM). Given a classification task, infoCAM depict how to accumulate information of partial regions to that of the entire image toward a label. Thus, we can utilise infoCAM to locate the most informative features for a label. When applied to an image classification task, infoCAM performs better than the traditional classification map in the weakly supervised object localisation task. We achieve state-of-the-art results on Tiny-ImageNet.

Abstract (translated)

URL

https://arxiv.org/abs/2106.10472

PDF

https://arxiv.org/pdf/2106.10472.pdf

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