CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation 事件

CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation arXiv:2605.24807v1 Announce Type: new Abstract: Promptable foundation models such as the Segment Anything Model (SAM) produce high-quality masks but remain semantically blind, relying on external prompts to specify categories. Existing vision-language approaches address this limitation by using external prompt coupling, where a vision-language model generates spatial prompts for SAM as a separate stage. We