29 citations · 148 across the 21 of their papers we have counts for
7 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…
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…
Visual Answer Localization with Cross-modal Mutual Knowledge Transfer
Yixuan Weng, Bin Li
The goal of visual answering localization (VAL) in the video is to obtain a relevant and concise time clip from a video as the answer to the given natural language question. Early…
Learning to Locate Visual Answer in Video Corpus Using Question
Bin Li, Yixuan Weng, Bin Sun +1
We introduce a new task, named video corpus visual answer localization (VCVAL), which aims to locate the visual answer in a large collection of untrimmed instructional videos using…
Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video
Bin Li, Yixuan Weng, Bin Sun +1
The temporal answering grounding in the video (TAGV) is a new task naturally derived from temporal sentence grounding in the video (TSGV). Given an untrimmed video and a text quest…