11 citations · 20 across the 9 of their papers we have counts for
12 papers
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…
Towards Mitigating Deceptive Safety Alignment in Large Reasoning Models
Xiangyu Zhou, Saleh Zare Zade, Rafi Ibn Sultan +2
Large Reasoning Models (LRMs) are commonly trained with reinforcement learning (RL) to improve their generation of chain-of-thought (CoT) reasoning before producing final answers.…
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…
Fluence Map Prediction with Deep Learning: A Transformer-based Approach
Ujunwa Mgboh, Rafi Sultan, Dongxiao Zhu +1
Accurate fluence map prediction is essential in intensity-modulated radiation therapy (IMRT) to maximize tumor coverage while minimizing dose to healthy tissues. Conventional optim…
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…
FedDRO: Federated Compositional Optimization for Distributionally Robust Learning
Prashant Khanduri, Chengyin Li, Rafi Ibn Sultan +3
Recently, compositional optimization (CO) has gained popularity because of its applications in distributionally robust optimization (DRO) and many other machine learning problems.…
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…