2 citations · 2 across the 9 of their papers we have counts for
7 papers · 1 filter
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…
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…