  Image-level weakly supervised semantic segmentation is a challenging task
that has been deeply studied in recent years. Most of the common solutions
exploit class activation map (CAM) to locate object regions. However, such
response maps generated by the classification network usually focus on
discriminative object parts. In this paper, we propose a novel Co-Occurrent
Matching Network (COMNet), which can promote the quality of the CAMs and
enforce the network to pay attention to the entire parts of objects.
Specifically, we perform inter-matching on paired images that contain common
classes to enhance the corresponded areas, and construct intra-matching on a
single image to propagate the semantic features across the object regions. The
experiments on the Pascal VOC 2012 and MS-COCO datasets show that our network
can effectively boost the performance of the baseline model and achieve new
state-of-the-art performance.
