Awesome

Sambor

Boosting Segment Anything Model Towards Open-Vocabulary Learning<br> Xumeng Han, Longhui Wei, Xuehui Yu, Zhiyang Dou, Xin He, Kuiran Wang, Zhenjun Han, Qi Tian<br>

Method

<em> Overall architecture of Sambor. (Left) We construct a SideFormer to extract features from SAM and inject CLIP visual features to enhance semantic understanding. Building upon a two-stage detector, we devise an Open-set RPN that augments the vanilla RPN with open-set proposals generated by SAM. The language branch of CLIP encodes concepts in parallel, thereby empowering the detector with open-vocabulary recognition. (Right) The specific implementations of the extractor and injector in SideFormer. </em>