  Various research studies indicate that action recognition performance highly
depends on the types of motions being extracted and how accurate the human
actions are represented. In this paper, we investigate different optical flow,
and features extracted from these optical flow that capturing both short-term
and long-term motion dynamics. We perform power normalization on the magnitude
component of optical flow for flow dynamics correction to boost subtle or
dampen sudden motions. We show that existing action recognition models which
rely on optical flow are able to get performance boosted with our corrected
optical flow. To further improve performance, we integrate our corrected flow
dynamics into popular models through a simple hallucination step by selecting
only the best performing optical flow features, and we show that by
'translating' the CNN feature maps into these optical flow features with
different scales of motions leads to the new state-of-the-art performance on
several benchmarks including HMDB-51, YUP++, fine-grained action recognition on
MPII Cooking Activities, and large-scale Charades.
