Affiliation:
1. Renmin University of China, Haidian Qu, Beijing Shi, China
Abstract
Weakly Supervised Semantic Segmentation with image-level annotation uses localization maps from the classifier to generate pseudo labels. However, such localization maps focus only on sparse salient object regions, it is difficult to generate high-quality segmentation labels, which deviates from the requirement of semantic segmentation. To address this issue, we propose a dual-aware domain mining and cross-aware supervision (DDMCAS) method for weakly-supervised semantic segmentation. Specifically, we propose a dual-aware domain mining (DDM) module consisting of graph-based global reasoning unit and salient-region extension controller, which produces dense localization maps by exploring object features in salient regions and adjacent non-salient regions simultaneously. In order to further bridge the gap between salient regions and adjacent non-salient regions to generate more refined localization maps, we propose a cross-aware supervision (CAS) strategy to recover missing parts of the target objects and enhance weak attention in adjacent non-salient regions, leading to pseudo labels of higher quality for training the segmentation network. Based on the generated pseudo-labels, extensive experiments on PASCAL VOC 2012 dataset demonstrate that our method outperforms state-of-the-art methods using image-level labels for weakly supervised semantic segmentation.
Funder
National Natural Science Foundation of China
National Social Science Foundation of China
Research Seed Funds of School of Interdisciplinary Studies of Renmin University of China
Opening Project of State Key Laboratory of Digital Publishing Technology of Founder Group
Publisher
Association for Computing Machinery (ACM)