11 citations · 25 across the 12 of their papers we have counts for
14 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…
Partition-Aware Unlearning for Removing Spurious Correlations in Large Vision-Language Models
Aditi Sarker, Nazreen Shah, Rafi Ibn Sultan +3
Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background correlations, resulting in pred…
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…
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…
Not All Tokens Are Meant to Be Forgotten
Xiangyu Zhou, Yao Qiang, Saleh Zare Zade +3
Large Language Models (LLMs), pre-trained on massive text corpora, exhibit remarkable human-level language understanding, reasoning, and decision-making abilities. However, they te…
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…
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…
Learning to Poison Large Language Models for Downstream Manipulation
Xiangyu Zhou, Yao Qiang, Saleh Zare Zade +4
The advent of Large Language Models (LLMs) has marked significant achievements in language processing and reasoning capabilities. Despite their advancements, LLMs face vulnerabilit…
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…
Interpretability-Aware Vision Transformer
Yao Qiang, Chengyin Li, Prashant Khanduri +1
Vision Transformers (ViTs) have become prominent models for solving various vision tasks. However, the interpretability of ViTs has not kept pace with their promising performance.…
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…
Fairness-aware Vision Transformer via Debiased Self-Attention
Yao Qiang, Chengyin Li, Prashant Khanduri +1
Vision Transformer (ViT) has recently gained significant attention in solving computer vision (CV) problems due to its capability of extracting informative features and modeling lo…