2 citations · 2 across the 12 of their papers we have counts for
13 papers
SER: Learning to Ground Video Reasoning with Semantic Evidence Rewards
Sheng Xia, Zhengqin Lai, Tianxiang Jiang +4
Video MLLMs often struggle with fine-grained spatio-temporal reasoning, sometimes generating correct answers based on irrelevant frames or objects. Although outputting spatio-tempo…
DV-VLN: Dual Verification for Reliable LLM-Based Vision-and-Language Navigation
Zijun Li, Shijie Li, Zhenxi Zhang +2
Vision-and-Language Navigation (VLN) requires an embodied agent to navigate in a complex 3D environment according to natural language instructions. Recent progress in large languag…
M-Med: A Benchmark for Multi-lingual, Multi-modal, and Multi-hop Reasoning in Medical Instructional Video Understanding
Shenxi Liu, Kan Li, Mingyang Zhao +5
With the rapid progress of artificial intelligence (AI) in multi-modal understanding, there is increasing potential for video comprehension technologies to support professional dom…
Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge
Bin Li, Shenxi Liu, Yixuan Weng +3
Following the successful hosts of the 1-st (NLPCC 2023 Foshan) CMIVQA and the 2-rd (NLPCC 2024 Hangzhou) MMIVQA challenges, this year, a new task has been introduced to further adv…
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…
VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with AtrousLoRA
Adnan Iltaf, Rayan Merghani Ahmed, Zhenxi Zhang +2
Medical image segmentation is crucial for clinical diagnosis and treatment planning, especially when dealing with complex anatomical structures such as vessels. However, accurately…
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…
SpineMamba: Enhancing 3D Spinal Segmentation in Clinical Imaging through Residual Visual Mamba Layers and Shape Priors
Zhiqing Zhang, Tianyong Liu, Guojia Fan +3
Accurate segmentation of 3D clinical medical images is critical in the diagnosis and treatment of spinal diseases. However, the inherent complexity of spinal anatomy and uncertaint…
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation
Zhiqing Zhang, Guojia Fan, Tianyong Liu +5
Medical image segmentation is critical for diagnosing and treating spinal disorders. However, the presence of high noise, ambiguity, and uncertainty makes this task highly challeng…