9 citations · 9 across the 7 of their papers we have counts for
7 papers
Hallucination Mitigation for Large Vision-Language Models via Implicit Feature Stabilization
Aditi Sarker, Rafi Ibn Sultan, Hui Zhu +2
Large Vision-Language Models (LVLMs) are prone to hallucinations: they fluently describe objects, attributes, and scenes that are not in the image. We connect part of this failure…
MedPlex: Deep Vision-Language Co-Adaptation for Clinically Grounded Medical Segmentation
Rafi Ibn Sultan, Hui Zhu, Chengyin Li +1
Medical image segmentation is still largely treated as a vision-only problem, although clinical interpretation often relies on textual knowledge of anatomy, location, appearance, a…
WalkGPT: Grounded Vision-Language Conversation with Depth-Aware Segmentation for Pedestrian Navigation
Rafi Ibn Sultan, Hui Zhu, Xiangyu Zhou +4
Ensuring accessible pedestrian navigation requires reasoning about both semantic and spatial aspects of complex urban scenes, a challenge that existing Large Vision-Language Models…
Automatic Calibration for Membership Inference Attack on Large Language Models
Saleh Zare Zade, Yao Qiang, Xiangyu Zhou +4
Membership Inference Attacks (MIAs) have recently been employed to determine whether a specific text was part of the pre-training data of Large Language Models (LLMs). However, exi…
BiPVL-Seg: Bidirectional Progressive Vision-Language Fusion with Global-Local Alignment for Medical Image Segmentation
Rafi Ibn Sultan, Hui Zhu, Chengyin Li +1
Medical image segmentation typically relies solely on visual data, overlooking the rich textual information clinicians use for diagnosis. Vision-language models attempt to bridge t…
MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training
Chengyin Li, Hui Zhu, Rafi Ibn Sultan +5
In the diverse field of medical imaging, automatic segmentation has numerous applications and must handle a wide variety of input domains, such as different types of Computed Tomog…
GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation
Rafi Ibn Sultan, Chengyin Li, Hui Zhu +3
In geographical image segmentation, performance is often constrained by the limited availability of training data and a lack of generalizability, particularly for segmenting mobili…