Videos are a commonly-used type of content in learning during Web search. Many e-learning platforms provide quality content, but sometimes educational videos are long and cover many topics. Humans are good in extracting important sections from videos, but it remains a significant challenge for computers. In this paper, we address the problem of assigning importance scores to video segments, that is how much information they contain with respect to the overall topic of an educational video. We present an annotation tool and a new dataset of annotated educational videos collected from popular online learning platforms. Moreover, we propose a multimodal neural architecture that utilizes state-of-the-art audio, visual and textual features. Our experiments investigate the impact of visual and temporal information, as well as the combination of multimodal features on importance prediction.
MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data
PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）
Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁大学医学院）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Chinese Academy of Sciences（中国科学院）
Ref-AVS: Refer and Segment Objects in Audio-Visual Scenes
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Edu-MMBias: A Three-Tier Multimodal Benchmark for Auditing Social Bias in Vision-Language Models under Educational Contexts
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（东华师范大学教育人工智能研究所）；Faculty of Education, East China Normal University（东华师范大学教育学院）
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion
The Chinese University of Hong Kong（香港中文大学）；Imperial College Longdon（伦敦帝国理工学院）；Imsight Medical Technology Co. Ltd.（艾迈医疗科技有限公司）；The Hong Kong Polytechnic University（香港理工大学）；Chinese Academy of Sciences（中国科学院）
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task
Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Northwestern Polytechnical University（西北工业大学）；University of Pittsburgh（匹兹堡大学）；University of Sydney（悉尼大学）；JD Finance America Corporation（京东金融美国公司）
Joint Segmentation and Grading with Iterative Optimization for Multimodal Glaucoma Diagnosis
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China（香港大学电子与电气工程系）；College of Information Engineering, Zhejiang University of Technology, Hangzhou, China（浙江工业大学信息工程学院）
Segment Any Architectural Facades (SAAF):An automatic segmentation model for building facades, walls and windows based on multimodal semantics guidance
National University of Singapore（新加坡国立大学）；Guangzhou University（广州大学）；Tsinghua University（清华大学）；South China University of Technology（华南理工大学）
Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review
Department of Computer Science Engineering, Universidad Autónoma de Madrid（马德里自治大学计算机科学工程系）；Digital Futures Institute, Teachers College Columbia University（哥伦比亚大学教师学院数字未来研究所）
OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 Mobility学院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通全国重点实验室）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
SlideChain: Semantic Provenance for Lecture Understanding via Blockchain Registration
Department of Computer Science Rensselaer Polytechnic Institute（计算机科学系罗切斯特理工学院）；Department of Biomedical Engineering Rensselaer Polytechnic Institute（生物医学工程系罗切斯特理工学院）
LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing
State Key Laboratory of information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Electronic Information School, Wuhan University（武汉大学电子信息学院）
Can MLLMs Read Students' Minds? Unpacking Multimodal Error Analysis in Handwritten Math
Lehigh University（里海大学）；Squirrel Ai Learning（松鼠AI学习）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Michigan State University（密歇根州立大学）
Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos
TIB – Leibniz Information Centre for Science and Technology（蒂宾根-莱比锡信息科学与技术研究中心）；Hochschule Hannover – Data（汉诺威高等学院-数据）；H Institute for Applied Data Science（汉诺威应用数据科学研究所）；L3S Research Center – Leibniz University Hannover（L3S研究中心-汉诺威莱比锡大学）
