Weakly Supervised Segmentation with Maximum Bipartite Graph Matching

Abstract

In the weakly supervised segmentation task with only image-level labels, a common step in many existing algorithms is first to locate the image regions corresponding to each existing class with the Class Activation Maps (CAMs), and then generate the pseudo ground truth masks based on the CAMs to train a segmentation network in the fully supervised manner. The quality of the CAMs has a crucial impact on the performance of the segmentation model. We propose to improve the CAMs from a novel graph perspective. We model paired images containing common classes with a bipartite graph and use the maximum matching algorithm to locate corresponding areas in two images. The matching areas are then used to refine the predicted object regions in the CAMs. The experiments on Pascal VOC 2012 dataset show that our network can effectively boost the performance of the baseline model and achieves new state-of-the-art performance.