Navigating Instructional Videos: Efficient Retrieval of Relevant Detour Segments

Core Concepts

The core message of this work is to enable personalized query-based navigation of instructional videos by retrieving relevant "detour" segments from a large repository of how-to videos that satisfy the requested alteration to the current execution path.

Abstract

This paper introduces the novel "video detours" problem for navigating instructional videos. Given a source video and a natural language query asking to alter the how-to video's current path of execution in a certain way, the goal is to find a related "detour video" that satisfies the requested alteration.
The authors propose VidDetours, a video-language approach that learns to retrieve the targeted temporal segments from a large repository of how-to videos using video-and-text conditioned queries. They devise a language-based pipeline that exploits how-to video narration text to create weakly supervised training data.
The paper demonstrates the idea applied to the domain of how-to cooking videos, where a user can detour from their current recipe to find steps with alternate ingredients, tools, and techniques. Validating on a ground truth annotated dataset of 16K samples, the authors show their model's significant improvements over best available methods for video retrieval and question answering, with recall rates exceeding the state of the art by 35%.
The key contributions are the innovative task definition, the video-language model to address it, and the high quality evaluation set and benchmark. These results help pave the way towards an interconnected how-to video knowledge base that would transcend the expertise of any one teacher, weaving together the myriad of steps, tips, and strategies available in existing large-scale video content.

Stats

There are 370K cooking videos in the HowTo100M dataset used for training.
The weakly-supervised training set Dtr_D contains 586,603 training and 18,308 validation detour annotation tuples.
The manually annotated test set Dte_D contains 16,207 detour instances across 3,873 unique videos and 1,080 recipes/tasks.

Quotes

"What if the wealth of knowledge in online instructional videos was not an array of isolated lessons, but instead an interconnected network of information?"
"Conditioned on the content watched so far in the source video, the goal is to identify a detour video—and a temporal segment within it—that would allow the user to continue their task with the adjustment specified by their language query, then return to the original source video and complete execution."

Key Insights Distilled From

Detours for Navigating Instructional Videos

by Kumar Ashuto... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2401.01823.pdf

Deeper Inquiries

How could the proposed video detours framework be extended to other domains beyond cooking, such as home repair, sports, or creative hobbies

The proposed video detours framework can be extended to other domains beyond cooking by adapting the model to understand the specific characteristics and requirements of each domain. For example, in home repair videos, the system could be trained to recognize tools, materials, and techniques commonly used in DIY projects. For sports instructional videos, the model could focus on recognizing different types of exercises, equipment, and movements. In creative hobbies like painting or crafting, the system could learn to identify various materials, techniques, and artistic styles. By training the model on a diverse range of instructional video content from different domains, it can develop a comprehensive understanding of various tasks and activities, enabling users to navigate through different types of videos with ease.

What are the potential challenges in scaling the weakly-supervised data generation approach to handle a broader range of queries and video content

Scaling the weakly-supervised data generation approach to handle a broader range of queries and video content may pose several challenges. One challenge is ensuring the quality and relevance of the generated detour annotations, especially for more complex or niche topics. Generating accurate detour queries that capture the nuances of different tasks and activities across various domains can be challenging and may require more sophisticated language models. Additionally, scaling the approach to handle a larger volume of video content and queries would require significant computational resources and efficient data processing pipelines. Ensuring the diversity and representativeness of the training data across different domains is crucial to building a robust and generalizable video detours system.

How could the video detours system be integrated with a larger instructional video platform to enable seamless navigation and personalized learning experiences for users

Integrating the video detours system with a larger instructional video platform can enhance the user experience and provide personalized learning opportunities. By incorporating the detour functionality into the platform, users can seamlessly navigate between related videos, explore alternative methods, and customize their learning paths based on their preferences and requirements. The system could offer personalized recommendations, suggest detours based on user queries, and track user progress across different videos. Implementing features like bookmarking, history tracking, and user feedback mechanisms can further enhance the user experience and facilitate continuous learning. By integrating the video detours system with a larger instructional video platform, users can access a more interactive and adaptive learning environment tailored to their individual needs.