An event happening in the world is often made of different activities and actions that can unfold simultaneously or sequentially within a few seconds. However, most large-scale datasets built to train models for action recognition provide a single label per video clip. Consequently, models can be incorrectly penalized for classifying actions that exist in the videos but are not explicitly labeled and do not learn the full spectrum of information that would be mandatory to more completely comprehend different events and eventually learn causality between them. Towards this goal, we augmented the existing video dataset, Moments in Time (MiT), to include over two million action labels for over one million three second videos. This multi-label dataset introduces novel challenges on how to train and analyze models for multi-action detection. Here, we present baseline results for multi-action recognition using loss functions adapted for long tail multi-label learning and provide improved methods for visualizing and interpreting models trained for multi-label action detection.
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