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Found 2 papers, 1 papers with code
Tuning-free Universally-Supervised Semantic Segmentation
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.
Foundation Model Assisted Weakly Supervised Semantic Segmentation
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.
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