Weakly-supervised Semantic Segmentation via Dual-stream Contrastive Learning of Cross-image Contextual Information

arXiv:2405.04913v1 Announce Type: new
Abstract: 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: …

abstract arxiv cs.cv current deep learning gap image information performance research segmentation semantic tags type via weakly-supervised