Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization

TL;DR

This paper introduces an anti-adversarial attribution method that enhances class-relevant feature localization, significantly improving weakly supervised semantic segmentation and object localization performance.

Contribution

It proposes AdvCAM, an anti-adversarial manipulation of attribution maps, and a regularization technique to improve pixel-level localization from class labels.

Findings

Achieves state-of-the-art results on PASCAL VOC 2012 and MS COCO 2014 datasets.

Improves object localization accuracy on CUB-200-2011 and ImageNet-1K datasets.

Enhances non-discriminative features in attribution maps for better object coverage.

Abstract

Obtaining accurate pixel-level localization from class labels is a crucial process in weakly supervised semantic segmentation and object localization. Attribution maps from a trained classifier are widely used to provide pixel-level localization, but their focus tends to be restricted to a small discriminative region of the target object. An AdvCAM is an attribution map of an image that is manipulated to increase the classification score produced by a classifier before the final softmax or sigmoid layer. This manipulation is realized in an anti-adversarial manner, so that the original image is perturbed along pixel gradients in directions opposite to those used in an adversarial attack. This process enhances non-discriminative yet class-relevant features, which make an insufficient contribution to previous attribution maps, so that the resulting AdvCAM identifies more regions of the target object. In addition, we introduce a new regularization procedure that inhibits the incorrect attribution of regions unrelated to the target object and the excessive concentration of attributions on a small region of the target object. Our method achieves a new state-of-the-art performance in weakly and semi-supervised semantic segmentation, on both the PASCAL VOC 2012 and MS COCO 2014 datasets. In weakly supervised object localization, it achieves a new state-of-the-art performance on the CUB-200-2011 and ImageNet-1K datasets.