  Weakly supervised semantic segmentation (WSSS) has gained significant
popularity since it relies only on weak labels such as image level annotations
rather than pixel level annotations required by supervised semantic
segmentation (SSS) methods. Despite drastically reduced annotation costs,
typical feature representations learned from WSSS are only representative of
some salient parts of objects and less reliable compared to SSS due to the weak
guidance during training. In this paper, we propose a novel Multi-Strategy
Contrastive Learning (MuSCLe) framework to obtain enhanced feature
representations and improve WSSS performance by exploiting similarity and
dissimilarity of contrastive sample pairs at image, region, pixel and object
boundary levels. Extensive experiments demonstrate the effectiveness of our
method and show that MuSCLe outperforms the current state-of-the-art on the
widely used PASCAL VOC 2012 dataset.

Computer Vision and Pattern Recognition