GUIDE: A Guideline-Guided Dataset for Instructional Video Comprehension

TL;DR

GUIDE introduces a comprehensive dataset with task-level guidelines and annotations for instructional videos, enabling improved model understanding and generation of task descriptions, summaries, and guided captions.

Contribution

The paper presents a new dataset with task-level guidelines and annotations, along with benchmarks for instructional video comprehension tasks, addressing limitations of existing datasets.

Findings

Models can generate step captions and guidelines effectively.

Guideline-guided captioning improves task understanding.

GUIDE serves as a new benchmark for instructional video comprehension.

Abstract

There are substantial instructional videos on the Internet, which provide us tutorials for completing various tasks. Existing instructional video datasets only focus on specific steps at the video level, lacking experiential guidelines at the task level, which can lead to beginners struggling to learn new tasks due to the lack of relevant experience. Moreover, the specific steps without guidelines are trivial and unsystematic, making it difficult to provide a clear tutorial. To address these problems, we present the GUIDE (Guideline-Guided) dataset, which contains 3.5K videos of 560 instructional tasks in 8 domains related to our daily life. Specifically, we annotate each instructional task with a guideline, representing a common pattern shared by all task-related videos. On this basis, we annotate systematic specific steps, including their associated guideline steps, specific step descriptions and timestamps. Our proposed benchmark consists of three sub-tasks to evaluate comprehension ability of models: (1) Step Captioning: models have to generate captions for specific steps from videos. (2) Guideline Summarization: models have to mine the common pattern in task-related videos and summarize a guideline from them. (3) Guideline-Guided Captioning: models have to generate captions for specific steps under the guide of guideline. We evaluate plenty of foundation models with GUIDE and perform in-depth analysis. Given the diversity and practicality of GUIDE, we believe that it can be used as a better benchmark for instructional video comprehension.