2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CV2025
Re-purposing SAM into Efficient Visual Projectors for MLLM-Based Referring Image Segmentation
Xiaobo Yang, Xiaojin Gong
Recently, Referring Image Segmentation (RIS) frameworks that pair the Multimodal Large Language Model (MLLM) with the Segment Anything Model (SAM) have achieved impressive results.…
cs.CV2024★ 1 cited
Tuning-free Universally-Supervised Semantic Segmentation
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. Initially,…
cs.CV2023★ 2 cited
Foundation Model Assisted Weakly Supervised Semantic Segmentation
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 sema…