  Video is complex due to large variations in motion and rich content in
fine-grained visual details. Abstracting useful information from such
information-intensive media requires exhaustive computing resources. This paper
studies a two-step alternative that first condenses the video sequence to an
informative "frame" and then exploits off-the-shelf image recognition system on
the synthetic frame. A valid question is how to define "useful information" and
then distill it from a video sequence down to one synthetic frame. This paper
presents a novel Informative Frame Synthesis (IFS) architecture that
incorporates three objective tasks, i.e., appearance reconstruction, video
categorization, motion estimation, and two regularizers, i.e., adversarial
learning, color consistency. Each task equips the synthetic frame with one
ability, while each regularizer enhances its visual quality. With these, by
jointly learning the frame synthesis in an end-to-end manner, the generated
frame is expected to encapsulate the required spatio-temporal information
useful for video analysis. Extensive experiments are conducted on the
large-scale Kinetics dataset. When comparing to baseline methods that map video
sequence to a single image, IFS shows superior performance. More remarkably,
IFS consistently demonstrates evident improvements on image-based 2D networks
and clip-based 3D networks, and achieves comparable performance with the
state-of-the-art methods with less computational cost.
