  Thanks to the substantial and explosively inscreased instructional videos on
the Internet, novices are able to acquire knowledge for completing various
tasks. Over the past decade, growing efforts have been devoted to investigating
the problem on instructional video analysis. However, the most existing
datasets in this area have limitations in diversity and scale, which makes them
far from many real-world applications where more diverse activities occur. To
address this, we present a large-scale dataset named as "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 efficiently with a series of step labels
and the corresponding temporal boundaries. In order to provide a benchmark for
instructional video analysis, we evaluate plenty of approaches on the COIN
dataset under five different settings. Furthermore, we exploit two important
characteristics (i.e., task-consistency and ordering-dependency) for localizing
important steps in instructional videos. Accordingly, we propose two simple yet
effective methods, which can be easily plugged into conventional proposal-based
action detection models. We believe the introduction of the COIN dataset will
promote the future in-depth research on instructional video analysis for the
community. Our dataset, annotation toolbox and source code are available at
http://coin-dataset.github.io.
