  In recent years, deep neural network approaches have naturally extended to
the video domain, in their simplest case by aggregating per-frame
classifications as a baseline for action recognition. A majority of the work in
this area extends from the imaging domain, leading to visual-feature heavy
approaches on temporal data. To address this issue we introduce "Let's Dance",
a 1000 video dataset (and growing) comprised of 10 visually overlapping dance
categories that require motion for their classification. We stress the
important of human motion as a key distinguisher in our work given that, as we
show in this work, visual information is not sufficient to classify
motion-heavy categories. We compare our datasets' performance using imaging
techniques with UCF-101 and demonstrate this inherent difficulty. We present a
comparison of numerous state-of-the-art techniques on our dataset using three
different representations (video, optical flow and multi-person pose data) in
order to analyze these approaches. We discuss the motion parameterization of
each of them and their value in learning to categorize online dance videos.
Lastly, we release this dataset (and its three representations) for the
research community to use.
