Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (2103.08896v1)

Abstract: Weakly supervised semantic segmentation produces a pixel-level localization from a classifier, but it is likely to restrict its focus to a small discriminative region of the target object. AdvCAM is an attribution map of an image that is manipulated to increase the classification score. This manipulation is realized in an anti-adversarial manner, which perturbs the images along pixel gradients in the opposite direction from those used in an adversarial attack. It forces regions initially considered not to be discriminative to become involved in subsequent classifications, and produces attribution maps that successively identify 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 limits the attributions of the regions that already have high scores. On PASCAL VOC 2012 test images, we achieve mIoUs of 68.0 and 76.9 for weakly and semi-supervised semantic segmentation respectively, which represent a new state-of-the-art.

• The paper introduces AdvCAM, a novel method that employs anti-adversarial manipulation of attribution maps to enhance weakly and semi-supervised semantic segmentation while reducing the need for extensive pixel-level annotations.
• AdvCAM works by iteratively adjusting image pixels along gradient directions to strengthen class scores and uses regularization to refine attribution maps, avoiding incorrect class signals and overemphasis on single regions.
• Experimental results on the PASCAL VOC 2012 dataset show AdvCAM achieving state-of-the-art performance, reaching 68.0% mIoU for weakly supervised and 76.9% mIoU for semi-supervised settings without requiring extra network modification.

Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation

This paper addresses a significant challenge in semantic segmentation—the requirement for precise pixel-level annotations for training deep neural networks (DNNs). To reduce the annotation burden, the authors propose a novel method named AdvCAM, which improves weakly and semi-supervised semantic segmentation by manipulating attribution maps through an anti-adversarial process.

Method Overview

AdvCAM stands for Anti-Adversarially Manipulated Class Activation Maps. The core idea revolves around reversing adversarial attack methods to enhance the regions identified as being part of an object by a classification model. Unlike adversarial attacks, which perturb inputs to fool a model, the anti-adversarial manipulation by AdvCAM adjusts inputs to strengthen model classification decisions regarding relevant object features. This is achieved by iteratively perturbing image pixels along the gradient direction that increases the class-specific score, thereby extending the Regions of Discrimination (RoD) and improving the comprehensiveness of generated attribution maps.

Two regularization mechanisms are introduced to control the manipulation process:

1. Suppression of Attributions to Incorrect Classes: By diminishing the impact of regions that might incorrectly signal non-target class features, the method mitigates the risk of misclassification.
2. Restriction of Overemphasis on Discriminative Regions: By limiting the strength of attributions in already highly regarded regions, the procedure ensures the expansion of attribution maps covers previously non-discriminative but relevant regions.

Experimental Results

The experimental results demonstrate that AdvCAM sets a new benchmark for performance on the PASCAL VOC 2012 dataset with a mean Intersection over Union (mIoU) of 68.0% and 76.9% for weakly and semi-supervised settings, respectively. Notably, without requiring additional network modification, AdvCAM enhances state-of-the-art methods based on various weakly labeled datasets (such as image-level labels) and achieves results comparable to those methods relying on stronger supervision like bounding boxes.

Implications and Future Directions

This paper contributes significantly to both the practical and theoretical landscape:

• Practical Impact: AdvCAM paves the way for more accessible deployment of semantic segmentation systems by cutting down the labor-intensive annotation process, making supervised DNN models feasible for real-world applications where exhaustive labeling isn't viable.
• Theoretical Impact: The introduction of an anti-adversarial approach shifts the way researchers might think about neural network explainability and region of interest elucidation, offering an advancement in map interpretation and attribution handling.

Future research could delve into enhancing the computational efficiency of AdvCAM and exploring its amalgamation with other weakly supervised learning paradigms. Another promising avenue lies in expanding its application scope beyond visual domains, potentially impacting other structured prediction tasks in AI.

In conclusion, AdvCAM's method of leveraging anti-adversarial manipulations marks a significant stride toward more effective, label-efficient, and interpretable segmentation models. This trident approach—focusing on adversarial climbing, robust regularization strategies, and validation through empirical evidence—fortifies the potential of weakly supervised segmentation methodologies.