Average Activation Network for Weakly Supervised Semantic Segmentation

Abstract

Most current image-level weakly supervised semantic segmentation (WSSS) methods are based on class activation map (CAM). However, the main limitation of weakly supervised semantic segmentation is that the CAMs generated by the WSSS network always focus on the most discriminative parts of the object, limiting the CAM to capture the holistic object. So we propose an average activation network to generate CAMs of the holistic object with weakly supervision. We restrain the highest activation regions of the CAM by continuously splitting the training image on the maximum activation point of the CAM during the training process. In this way, our network pays more attention to the whole of the object. In addition, we propose a CAM similarity loss function to narrow the gap between fully-supervised semantic segmentation (FSSS) and WSSS. We conducted experiments on the PASCAL VOC 2012 dataset to validate the effectiveness of our method.