COIN is a large-scale instructional video dataset for comprehensive instructional video analysis. It contains 11,827 videos covering 180 different tasks organized into a 3-level hierarchical lexicon of 12 domains → tasks → steps. Each video is annotated with task/step labels and step-level temporal localization (start/end times), making the dataset suitable for tasks such as step (action) localization, procedural step recognition, action/segment classification and instructional-video understanding. The dataset was introduced by Tang et al. (CVPR 2019 / arXiv:1903.02874) and is distributed with an official website and GitHub repositories for annotations and code (coin-dataset.github.io, github.com/coin-dataset).
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