Weakly supervised semantic segmentation (WSSS) aims at learning a semantic
segmentation model with only image-level tags. Despite intensive research on
deep learning approaches over a decade, there is still a significant
performance gap between WSSS and full semantic segmentation. Most current WSSS
methods always focus on a limited single image (pixel-wise) information while
ignoring the valuable inter-image (semantic-wise) information. From this
perspective, a novel end-to-end WSSS framework called DSCNet is developed along
with two innovations: i) pixel-wise group contrast and semantic-wise graph
contrast are proposed and introduced into the WSSS framework; ii) a novel
dual-stream contrastive learning (DSCL) mechanism is designed to jointly handle
pixel-wise and semantic-wise context information for better WSSS performance.
Specifically, the pixel-wise group contrast learning (PGCL) and semantic-wise
graph contrast learning (SGCL) tasks form a more comprehensive solution.
Extensive experiments on PASCAL VOC and MS COCO benchmarks verify the
superiority of DSCNet over SOTA approaches and baseline models.

提出了一种新颖的端到端的弱监督语义分割框架 DSCNet，通过引入像素级组对比和语义级图对比以及设计一种新的双流对比学习机制，综合处理像素级和语义级上下文信息，从而在弱监督语义分割任务中取得了优于现有方法和基准模型的结果。