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

Understand

Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds.

• However, most large-scale datasets built to train models for action recognition in video only provide a single label per video.
• 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 present in each video in training.
• Towards this goal, we present the Multi-Moments in Time dataset (M-MiT) which includes over two million action labels for over one million three second videos.