DDAug: Differentiable Data Augmentation for Weakly Supervised Semantic Segmentation
Boyang Li, Fei Zhang, Longguang Wang, Yingqian Wang, Ting Liu, Zaiping Lin, Wei Ke An, Yulan Guo · IEEE Transactions on Multimedia · 2023
Weakly supervised semantic segmentation(WSSS) with image-level labels has witnessed promising advances with the help ofclass activation maps(CAM). However, CAM is always confined to small discriminative seed regions due to its simple classification loss guided training manner. To handle this problem, recent works introduced specifically designed regularizations and modules to expand the CAM seed regions, serving as the final segmentation masks. In this paper, we surprisingly find that the classification loss could suppress the gains from these regularization and modules in the late training phase, thereby limiting the further growth of CAM, which we call as theexplicit supervision disturb(ESD) issue. Interestingly, we find that specificdata augmentation(DA) operations (e.g., CutMix) can relieve such ESD issue, and the benefits introduced by different DA operations vary a lot. To maximize the benefits, we proposedifferentiable data augmentation(DDAug) to automatically search for the proper DA policy. Specifically, we design amulti-level search spaceto sequentially sample DA operations with different properties. Extensive experiments demonstrate that the proposed DDAug can alleviate the ESD issue and introduce consistent improvements to various popular WSSS methods, achieving the state-of-the-art performance on the MS COCO 2014 and PASCAL VOC 2012 datasets.