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

arXiv: https://arxiv.org/abs/2503.21130 · HTML: https://mlsanity.com/paper/2503.21130/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 65 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Hierarchical Video-Moment Retrieval and Step-Captioning](https://mlsanity.com/paper/2303.16406/) (2023)
- [TutoAI: A Cross-domain Framework for AI-assisted Mixed-media Tutorial Creation on Physical Tasks](https://mlsanity.com/paper/2403.08049/) (2024)
- [Supporting Business Document Workflows via Collection-Centric Information Foraging with Large Language Models](https://mlsanity.com/paper/2405.01501/) (2024)
- [VideoMap: Supporting Video Editing Exploration, Brainstorming, and Prototyping in the Latent Space](https://mlsanity.com/paper/2211.12492/) (2022)
- [YTCommentQA: Video Question Answerability in Instructional Videos](https://mlsanity.com/paper/2401.17343/) (2024)

## Built on by (most cited first, 3 of 3)

- [Vid2Coach: Transforming How-To Videos into Task Assistants](https://mlsanity.com/paper/2506.00717/) (2025)
- [NoteIt: A System Converting Instructional Videos to Interactable Notes Through Multimodal Video Understanding](https://mlsanity.com/paper/2508.14395/) (2025)
- [AutoCue: Multimodal LLM-Assisted Externalization of Implicit Inputs as Instructional Visual Cues in Screencast Tutorials](https://mlsanity.com/paper/2608.04910/) (2026)

## 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.