  We present the Moments in Time Dataset, a large-scale human-annotated
collection of one million short videos corresponding to dynamic events
unfolding within three seconds. Modeling the spatial-audio-temporal dynamics
even for actions occurring in 3 second videos poses many challenges: meaningful
events do not include only people, but also objects, animals, and natural
phenomena; visual and auditory events can be symmetrical in time ("opening" is
"closing" in reverse), and either transient or sustained. We describe the
annotation process of our dataset (each video is tagged with one action or
activity label among 339 different classes), analyze its scale and diversity in
comparison to other large-scale video datasets for action recognition, and
report results of several baseline models addressing separately, and jointly,
three modalities: spatial, temporal and auditory. The Moments in Time dataset,
designed to have a large coverage and diversity of events in both visual and
auditory modalities, can serve as a new challenge to develop models that scale
to the level of complexity and abstract reasoning that a human processes on a
daily basis.
