2 citations · 4 across the 16 of their papers we have counts for
8 papers · 1 filter
Attention-Guided Flow-Matching for Sparse 3D Geological Generation
Zhixiang Lu, Mengqi Han, Peixin Guo +4
Constructing high-resolution 3D geological models from sparse 1D borehole and 2D surface data is a highly ill-posed inverse problem. Traditional heuristic and implicit modeling met…
Semantic-Topological Graph Reasoning for Language-Guided Pulmonary Screening
Chenyu Xue, Yiran Liu, Mian Zhou +2
Medical image segmentation driven by free-text clinical instructions is a critical frontier in computer-aided diagnosis. However, existing multimodal and foundation models struggle…
ConFoThinking: Consolidated Focused Attention Driven Thinking for Visual Question Answering
Zhaodong Wu, Haochen Xue, Qi Cao +5
Thinking with Images improves fine-grained VQA for MLLMs by emphasizing visual cues. However, tool-augmented methods depend on the capacity of grounding, which remains unreliable f…
SAM-DCE: Decoupled Context Enhancement for Prompt-Free Medical Image Segmentation
Yingzhen Hu, Yiheng Zhong, Ruobing Li +8
The Segment Anything Model (SAM) is a promising foundation model for medical image segmentation, but its prompt-driven design is not directly suitable for fully automatic multi-cla…
Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
Feilong Tang, Chengzhi Liu, Zhongxing Xu +9
Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often suffer from hallucinations…
Beyond Words: AuralLLM and SignMST-C for Sign Language Production and Bidirectional Accessibility
Yulong Li, Yuxuan Zhang, Feilong Tang +10
Sign language is the primary communication mode for 72 million hearing-impaired individuals worldwide, necessitating effective bidirectional Sign Language Production and Sign Langu…
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation
Feilong Tang, Zhongxing Xu, Ming Hu +6
In medical image analysis, multi-organ semi-supervised segmentation faces challenges such as insufficient labels and low contrast in soft tissues. To address these issues, existing…
Toward Modality Gap: Vision Prototype Learning for Weakly-supervised Semantic Segmentation with CLIP
Zhongxing Xu, Feilong Tang, Zhe Chen +5
The application of Contrastive Language-Image Pre-training (CLIP) in Weakly Supervised Semantic Segmentation (WSSS) research powerful cross-modal semantic understanding capabilitie…