29 citations · 148 across the 21 of their papers we have counts for
6 papers · 1 filter
ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant
Yifan Xiang, Zhenxi Zhang, Bin Li +6
Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-specific concepts and generate contextual c…
Ask2Loc: Learning to Locate Instructional Visual Answers by Asking Questions
Chang Zong, Bin Li, Shoujun Zhou +2
Locating specific segments within an instructional video is an efficient way to acquire guiding knowledge. Generally, the task of obtaining video segments for both verbal explanati…
Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
Junkai Zhang, Bin Li, Shoujun Zhou +1
Medical Visual Question Answering (Med-VQA) answers clinical questions using medical images, aiding diagnosis. Designing the MedVQA system holds profound importance in assisting cl…
MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation
Rayan Merghani Ahmed, Adnan Iltaf, Mohamed Elmanna +5
Accurate segmentation of coronary Digital Subtraction Angiography (DSA) images is essential for diagnosing and treating coronary artery disease (CAD). Despite advances in deep lear…
Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs
Haoran Fan, Bin Li, Yixuan Weng +1
While LLMs have demonstrated remarkable potential in time series forecasting, their practical deployment remains constrained by excessive computational demands and memory footprint…
ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images
Hongyu Ge, Longkun Hao, Zihui Xu +5
Medical Visual Question Answering (Med-VQA) represents a critical and challenging subtask within the general VQA domain. Despite significant advancements in general VQA, multimodal…