Zhifen Yan

SAMIX: Reinforcing SAM2 with Semantic Adapter and Reference Selecting Policy for Mix-Supervised Segmentation

Mix-supervised image segmentation aims to effectively leverage heterogeneous annotations. Recent prompt-based advances utilize foundation models such as Segment Anything Model (SAM) to generate pseudo-masks by treating weak labels as spatial prompts. However, these methods rely heavily on sparse spa