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.
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
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:2604. 01280v2 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence.
By Marco Morini, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
arXiv:2601. 02580v2 Announce Type: replace-cross Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration.
By Christopher Ormerod
arXiv:2607. 25294v1 Announce Type: cross Abstract: Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge.
By Lai Wei, Chengqi Li, Jiapeng Li, Ruina Hu, Yue Wang, Weiran Huang