A dataset of video clips with spoken and visual attributes

Dataset has been designed for the activity classification task of 31 activities. The videos were clipped per activity, resulting in a total of 2,000 short RGB+D video samples. Activities were performed in a natural manner. As a result, the dataset poses a unique combination of challenges: high intra-class variation, high-class imbalance, and activities with similar motion and high duration variance. Example: Make coffee, pour water, add sugar, etc.