Weakly-supervised Semantic Segmentation via Dual-stream Contrastive Learning of Cross-image Contextual Information
<|reference_start|>Weakly-supervised Semantic Segmentation via Dual-stream Contrastive Learning of Cross-image Contextual Information: 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.<|reference_end|>
@article{lai2024weakly-supervised,
    title={Weakly-supervised Semantic Segmentation via Dual-stream Contrastive
  Learning of Cross-image Contextual Information},
    author={Qi Lai, Chi-Man Vong},
    journal={arXiv preprint arXiv:2405.04913},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04913},
    primaryClass={cs.CV}
}