mlsanityData to 7 Oct 2026

VideoMix: Aggregating How-To Videos for Task-Oriented Learning

arXiv 2503.21130 ↗First posted 27 Mar 2025cs.HC cs.CVcited by 3

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Abstract (arXiv)

Tutorial videos are a valuable resource for people looking to learn new tasks. People often learn these skills by viewing multiple tutorial videos to get an overall understanding of a task by looking at different approaches to achieve the task. However, navigating through multiple videos can be time-consuming and mentally demanding as these videos are scattered and not easy to skim. We propose VideoMix, a system that helps users gain a holistic understanding of a how-to task by aggregating information from multiple videos on the task. Insights from our formative study (N=12) reveal that learners value understanding potential outcomes, required materials, alternative methods, and important details shared by different videos. Powered by a Vision-Language Model pipeline, VideoMix extracts and organizes this information, presenting concise textual summaries alongside relevant video clips, enabling users to quickly digest and navigate the content. A comparative user study (N=12) demonstrated that VideoMix enabled participants to gain a more comprehensive understanding of tasks with greater efficiency than a baseline video interface, where videos are viewed independently. Our findings highlight the potential of a task-oriented, multi-video approach where videos are organized around a shared goal, offering an enhanced alternative to conventional video-based learning.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 65 references that are in the map. First posted on arXiv 27 Mar 2025.

Builds on

7 of 65 references.

References · 7

  1. 2019HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clipscited by 946
  2. 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 320
  3. 2023Hierarchical Video-Moment Retrieval and Step-Captioningcited by 83
  4. 2024TutoAI: A Cross-domain Framework for AI-assisted Mixed-media Tutorial Creation on Physical Taskscited by 6
  5. 2024Supporting Business Document Workflows via Collection-Centric Information Foraging with Large Language Modelscited by 6
  6. 2022VideoMap: Supporting Video Editing Exploration, Brainstorming, and Prototyping in the Latent Spacecited by 4
  7. 2024YTCommentQA: Video Question Answerability in Instructional Videoscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

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  1. 2025InstructionBench: An Instructional Video Understanding Benchmark
  2. 2025VEGGIE: Instructional Editing and Reasoning Video Concepts with Grounded Generation
  3. 2026Video-IFBench: Evaluating Instruction Following of Multimodal LLMs in Video Understanding Scenarios
  4. 2026SemComp-Bench: Benchmarking Semantic Task Completion in Video Generation
  5. 2024ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions
  6. 2026Less is More: Label-Guided Summarization of Procedural and Instructional Videos
  7. 2024MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding
  8. 2026GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation