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Videos capture events that typically contain multiple sequential, andsimultaneous, actions even in the span of only a few seconds. However, mostlarge-scale datasets built to train models for action recognition in video onlyprovide a single label per video. Consequently, models can be incorrectlypenalized for classifying actions that exist in the videos but are notexplicitly labeled and do not learn the full spectrum of information present ineach video in training. Towards this goal, we present the Multi-Moments in Timedataset (M-MiT) which includes over two million action labels for over onemillion three second videos. This multi-label dataset introduces novelchallenges on how to train and analyze models for multi-action detection. Here,we present baseline results for multi-action recognition using loss functionsadapted for long tail multi-label learning, provide improved methods forvisualizing and interpreting models trained for multi-label action detectionand show the strength of transferring models trained on M-MiT to smallerdatasets.

Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding