To mitigate the necessity for large amounts of supervised segmentation annotation sets, multiple Weakly Supervised Semantic Segmentation (WSSS) strategies have been devised. These will often rely on advanced data and model regularization strategies to instigate the development of useful properties (e.g., prediction completeness and fidelity to semantic boundaries) in segmentation priors, notwithstanding the lack of annotated information. In this work, we first create a strong baseline by analyzing complementary WSSS techniques and regularizing strategies, considering their strengths and limitations. We then propose a new Class-specific Adversarial Erasing strategy, comprising two adversarial CAM generating networks being gradually refined to produce robust semantic segmentation proposals. Empirical results suggest that our approach induces substantial improvement in the effectiveness of the baseline, resulting in a noticeable improvement over both Pascal VOC 2012 and MS COCO 2014 datasets.

Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision. Most advanced approaches are based on class activation maps (CAMs) to generate pseudo-labels to train the segmentation network. The main limitation of WSSS is that the process of generating pseudo-labels from CAMs that use an image classifier is mainly focused on the most discriminative parts of the objects. To address this issue, we propose Puzzle-CAM, a process that minimizes differences between the features from separate patches and the whole image. Our method consists of a puzzle module and two regularization terms to discover the most integrated region in an object. Puzzle-CAM can activate the overall region of an object using image-level supervision without requiring extra parameters. % In experiments, Puzzle-CAM outperformed previous state-of-the-art methods using the same labels for supervision on the PASCAL VOC 2012 test dataset. In experiments, Puzzle-CAM outperformed previous state-of-the-art methods using the same labels for supervision on the PASCAL VOC 2012 dataset. Code associated with our experiments is available at https://github.com/OFRIN/PuzzleCAM.