Cycle-Consistent Learning for Weakly Supervised Semantic Segmentation

Bin Wang, Yu Qiao, Dahua Lin, Stephen D. H. Yang, Weijia Li

Abstract: We investigate a principle way to accomplish the weakly supervised semantic segmentation, only using scribbles as supervision. The key challenge of this task lies in how to accurately propagate the semantic labels from the annotated scribbles to those unlabeled regions so that accurate pseudo masks can be harvested to learn better segmentation models. To tackle this issue, we propose a simple, strong, and unified framework named Cycle-Consistent Learning (CCL) in this work. To be specific, our CCL first utilizes the given scribbles for training and makes a prediction for those unlabeled regions. Then, the predicted regions, in turn, serve as supervision for learning to predict the labeled scribbles. With such a cycle-consistent constraint, the accurate scribbles can reversely help ease those potential noises existing in the unlabeled regions, resulting in better pseudo masks. The training process of our CCL is looped until the network converges in an end-to-end way. We conduct extensive experiments on the popular PASCAL VOC benchmark and achieve a comparable result with the state-of-the-art method. The training mechanism of the CCL is straightforward and can be easily embedded into any future weakly supervised semantic segmentation approach.