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DDAug: Differentiable Data Augmentation for Weakly Supervised Semantic Segmentation
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DOI：10.1109/TMM.2023.3326300.png)
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Abstract
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Weakly supervised semantic segmentation (WSSS) with image-level labels has witnessed promising advances with the help of class 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 the explicit supervision disturb (ESD) issue. Interestingly, we find that specific data 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 propose differentiable data augmentation (DDAug) to automatically search for the proper DA policy. Specifically, we design a multi-level search space to 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.
Keywords:
Training
Electrostatic discharges
Data augmentation
Semantic segmentation
Semantics
Search problems
Market research
Deep learning
differentiable data augmentation
explicit supervision disturb
weakly supervised semantic segmentation
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