29 citations · 148 across the 21 of their papers we have counts for
21 papers
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…
Large Language Models Need Holistically Thought in Medical Conversational QA
Yixuan Weng, Bin Li, Fei Xia +5
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care. Despite the succes…
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
Yixuan Weng, Minjun Zhu, Fei Xia +4
Language models' (LMs) proficiency in handling deterministic symbolic reasoning and rule-based tasks remains limited due to their dependency implicit learning on textual data. To e…
Artificial Text Detection with Multiple Training Strategies
Bin Li, Yixuan Weng, Qiya Song +1
As the deep learning rapidly promote, the artificial texts created by generative models are commonly used in news and social media. However, such models can be abused to generate p…
Large Language Models are Better Reasoners with Self-Verification
Yixuan Weng, Minjun Zhu, Fei Xia +6
Recently, with the chain of thought (CoT) prompting, large language models (LLMs), e.g., GPT-3, have shown strong reasoning ability in several natural language processing tasks suc…
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…
Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation
Bin Li, Yixuan Weng, Ziyu Ma +2
This paper introduces the schemes of Team LingJing's experiments in NLPCC-2022-Shared-Task-4 Multi-modal Dialogue Understanding and Generation (MDUG). The MDUG task can be divided…
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
Fei Xia, Bin Li, Yixuan Weng +5
The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic. This paper presents a medical conversa…
Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages
Bin Li, Yixuan Weng, Fei Xia +1
The last decade has witnessed enormous improvements in science and technology, stimulating the growing demand for economic and cultural exchanges in various countries. Building a n…
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…
Prompt-based System for Personality and Interpersonal Reactivity Prediction
Bin Li, Yixuan Weng
This paper describes our proposed method for the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Personality Pre…
SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation
Bin Li, Fei Xia, Yixuan Weng +2
Acronym disambiguation means finding the correct meaning of an ambiguous acronym from the dictionary in a given sentence, which is one of the key points for scientific document und…
PSG: Prompt-based Sequence Generation for Acronym Extraction
Bin Li, Fei Xia, Yixuan Weng +3
Acronym extraction aims to find acronyms (i.e., short-forms) and their meanings (i.e., long-forms) from the documents, which is important for scientific document understanding (SDU…
A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging
Vaibhav Joshi, Sricharan Vijayarangan, Preejith SP +1
Automatic Sleep Staging study is presently done with the help of Electroencephalogram (EEG) signals. Recently, Deep Learning (DL) based approaches have enabled significant progress…
More but Correct: Generating Diversified and Entity-revised Medical Response
Bin Li, Encheng Chen, Hongru Liu +5
Medical Dialogue Generation (MDG) is intended to build a medical dialogue system for intelligent consultation, which can communicate with patients in real-time, thereby improving t…