There are substantial instructional videos on the Internet, which enables us
to acquire knowledge for completing various tasks. However, most existing
datasets for instructional video analysis have the limitations in diversity and
scale,which makes them far from many real-world applications where more diverse
activities occur. Moreover, it still remains a great challenge to organize and
harness such data. To address these problems, we introduce a large-scale
dataset called "COIN" for COmprehensive INstructional video analysis. Organized
with a hierarchical structure, the COIN dataset contains 11,827 videos of 180
tasks in 12 domains (e.g., vehicles, gadgets, etc.) related to our daily life.
With a new developed toolbox, all the videos are annotated effectively with a
series of step descriptions and the corresponding temporal boundaries.
Furthermore, we propose a simple yet effective method to capture the
dependencies among different steps, which can be easily plugged into
conventional proposal-based action detection methods for localizing important
steps in instructional videos. In order to provide a benchmark for
instructional video analysis, we evaluate plenty of approaches on the COIN
dataset under different evaluation criteria. We expect the introduction of the
COIN dataset will promote the future in-depth research on instructional video
analysis for the community.

介绍了一个名为 COIN 的大规模数据集，包含了 180 个任务的 11,827 个视频，使用层次结构进行组织，并用新开发的工具包有效注释了所有视频的一系列步骤描述和相应的时间边界，提出了一种简单但有效的方法，用于捕捉不同步骤之间的依赖关系，并可轻松插入传统的基于提议的动作检测方法中，以定位指导性视频中的重要步骤，并在不同的评估标准下在 COIN 数据集上评估了大量方法，以提供指导性视频分析的基准，旨在促进社区对指导性视频分析的未来深入研究。