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Cross-Pixel Dependency with Boundary-Feature Transformation for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation with image-level labels is a challenging problem that typically relies on the initial responses generated by the classification network to locate object regions. However, such initial responses only cover the most discriminative parts of the object and may incorrectly activate in the background regions. To address this problem, we propose a Cross-pixel Dependency with Boundary-feature Transformation (CDBT) method for weakly supervised semantic segmentation. Specifically, we develop a boundary-feature transformation mechanism, to build strong connections among pixels belonging to the same object but weak connections among different objects. Moreover, we design a cross-pixel dependency module to enhance the initial responses, which exploits context appearance information and refines the prediction of current pixels by the relations of global channel pixels, thus generating pseudo labels of higher quality for training the semantic segmentation network. Extensive experiments on the PASCAL VOC 2012 segmentation benchmark demonstrate that our method outperforms state-of-the-art methods using image-level labels as weak supervision.