Weakly Supervised Segmentation as Semantic-Based Regularization 事件

Weakly Supervised Segmentation as Semantic-Based Regularization arXiv:2605.13674v2 Announce Type: replace Abstract: Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations such as bounding boxes, scribbles, or image-level tags. While recent work leverages foundation models such as the Segment Anything Model (SAM) to generate pseudo-labels, these approaches typically depend on heuristic prompt choices and offer limited ways t