105 citations · 178 across the 5 of their papers we have counts for
6 papers
From Reasoning to Pixels: Grounded Medical Multimodal LLMs for VQA and Segmentation
Haowen Gu, Gensheng Pei, Junzhu Mao +3
Although Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in Medical Visual Question Answering (Med-VQA), their reliance on global image features o…
Universal Organizer of SAM for Unsupervised Semantic Segmentation
Tingting Li, Gensheng Pei, Xinhao Cai +3
Unsupervised semantic segmentation (USS) aims to achieve high-quality segmentation without manual pixel-level annotations. Existing USS models provide coarse category classificatio…
Semi-Supervised Semantic Segmentation With Region Relevance
Rui Chen, Tao Chen, Qiong Wang +1
Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones for the segmentation task. The most common approach is to gener…
FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network
Huafeng Liu, Pai Peng, Tao Chen +3
Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images. The current correlation-…
Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation
Tao Chen, Yazhou Yao, Lei Zhang +3
Weakly supervised semantic segmentation with only image-level labels aims to reduce annotation costs for the segmentation task. Existing approaches generally leverage class activat…
Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation
Tao Chen, Guosen Xie, Yazhou Yao +4
One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimi…