arXiv:2606. 07924v1 Announce Type: cross Abstract: This paper presents our system description for the 2nd Workshop on Multimodal Augmented Generation via MultimodAl Retrieval (MAGMaR).
By Jiaxin Dai, Zehang Wei, Jiamin Yan, Xiang Xiang
arXiv:2505.13520v2 Announce Type: replace-cross
Abstract: Textbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances hav...
By Hessa Alawwad, Usman Naseem, Areej Alhothali, Ali Alkhathlan, Amani Jamal
arXiv:2609.24083v1 Announce Type: new
Abstract: Generative AI enables scalable production of educational videos, but current systems largely focus on producing visually coherent content rather than s...
By Xinchen Ma, Shuimu Wang, Gaole He, Yanbin Zhang, Chunyang Wang, Yunshi Lan, Weining Qian
KnowVis is a framework that converts linear video lectures into knowledge‑centric visual narratives. It first extracts a detailed concept map from multimodal video content to identify key and challenging concepts, then builds structured knowledge units and synthesizes engaging visual summaries. The authors also provide a curated dataset of 125 educational videos across 10 disciplines, paired with 1,079 visual summaries, and show through automated evaluations and a human study that KnowVis produces more accurate, clear visuals that reduce cognitive load and improve learning effectiveness and knowledge retention.
By Yi Xu, Yifan Hou, Xiaoyu Zhang
arXiv:2602. 11790v2 Announce Type: replace Abstract: Although recent end-to-end video generation models demonstrate impressive performance in visually oriented content creation, they remain limited in scenarios that require strict logical rigor and precise knowledge representation, such as instructional and educational media.
By Lingyong Yan, Jiulong Wu, Dong Xie, Weixian Shi, Deguo Xia, Jizhou Huang
KnowVis is a framework that converts linear video lectures into knowledge‑centric visual summaries. It first builds a detailed concept map from multimodal video content to identify key and challenging concepts, then organizes these into structured knowledge units before synthesizing engaging visual narratives. The authors also provide a dataset of 125 educational videos with 1,079 visual summaries and show through automated metrics and a human study that KnowVis outperforms existing methods in accuracy, clarity, and learning outcomes.