Weakly Supervised Semantic Segmentation by a Class-Level Multiple Group Cosegmentation and Foreground Fusion Strategy

Fanman Meng, Kunming Luo, Hongliang Li, Qingbo Wu, Xiaolong Xu

Abstract: Weakly supervised semantic segmentation uses image-level labels to extract object regions. The existing methods focus on efficiently training CNN-based segmentation networks using the image-level labels. In contrast to the existing methods, this paper proposes a new fusion-based method, which first segments the foregrounds of each image by multiple group cosegmentation and then generates the semantic segmentation by combining the foregrounds. Specifically, a new CNN-based multiple group cosegmentation network is first proposed to segment foregrounds employing two cues, the discriminative cue and the local-to-global cue. Then, the fusion method is proposed to simply perform semantic segmentation based on the multiple group cosegmentation results. Experiments on the PASCAL VOC 2012 and MS COCO 2017 datasets demonstrate the effectiveness of the proposed method with mIoU values that are obviously larger than those of the existing methods.