11 citations · 20 across the 9 of their papers we have counts for
8 papers · 1 filter
Soft Spatial Reasoning
Rafi Ibn Sultan, Md. Sajid Alam Chowdhury, Saleh Zare Zade +4
Large Vision-Language Models (LVLMs) commonly perform spatial reasoning through chain-of-thought (CoT), encoding intermediate reasoning as autoregressive sequences of discrete lang…
SpatialCORE: Confidence-Aware Grounded Spatial Reasoning in Large Vision--Language Models
Rafi Ibn Sultan, Xiangyu Zhou, Md. Sajid Alam Chowdhury +4
Large Vision-Language Models (LVLMs) have made remarkable progress across visual perception tasks, yet spatial reasoning remains a persistent weakness, especially for questions tha…
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…
A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery
Rafi Ibn Sultan, Chengyin Li, Yiannos Demetriou +6
Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy.…
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…
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…
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…
AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation
Chengyin Li, Prashant Khanduri, Yao Qiang +3
Segment Anything Model (SAM) is one of the pioneering prompt-based foundation models for image segmentation and has been rapidly adopted for various medical imaging applications. H…