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.

为了解决互联网教学视频缺乏任务层面经验指导的问题，提出 GUIDE 数据集，其中包含 3.5K 个相关任务的 560 个指南，用于评估模型的理解能力，包括步骤字幕生成、指南摘要和指南引导字幕生成等三个子任务。