no code implementations • 23 May 2024 • Xiaobo Yang, Xiaojin Gong
This work presents a tuning-free semantic segmentation framework based on classifying SAM masks by CLIP, which is universally applicable to various types of supervision.
1 code implementation • 6 Dec 2023 • Xiaobo Yang, Xiaojin Gong
This work aims to leverage pre-trained foundation models, such as contrastive language-image pre-training (CLIP) and segment anything model (SAM), to address weakly supervised semantic segmentation (WSSS) using image-level labels.