Generic Event Boundary Detection

The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube. Source: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey

TAPOS is a new dataset developed on sport videos with manual annotations of sub-actions, and conduct a study on temporal action parsing on top. A sport activity usually consists of multiple sub-actions and that the awareness of such temporal structures is beneficial to action recognition. TAPOS contains 16,294 valid instances in total, across 21 action classes. These instances have a duration of 9.4 seconds on average. The number of instances within each class is different, where the largest class high jump has over 1,600 instances, and the smallest class beam has 200 instances. The average number of sub-actions also varies from class to class, where parallel bars has 9 sub-actions on average, and long jump has 3 sub-actions on average. All instances are split into train, validation and test sets, of sizes 13094, 1790, and 1763, respectively.