  The large amount of videos popping up every day, make it more and more
critical that key information within videos can be extracted and understood in
a very short time. Video summarization, the task of finding the smallest subset
of frames, which still conveys the whole story of a given video, is thus of
great significance to improve efficiency of video understanding. We propose a
novel Dilated Temporal Relational Generative Adversarial Network (DTR-GAN) to
achieve frame-level video summarization. Given a video, it selects the set of
key frames, which contain the most meaningful and compact information.
Specifically, DTR-GAN learns a dilated temporal relational generator and a
discriminator with three-player loss in an adversarial manner. A new dilated
temporal relation (DTR) unit is introduced to enhance temporal representation
capturing. The generator uses this unit to effectively exploit global
multi-scale temporal context to select key frames and to complement the
commonly used Bi-LSTM. To ensure that summaries capture enough key video
representation from a global perspective rather than a trivial randomly shorten
sequence, we present a discriminator that learns to enforce both the
information completeness and compactness of summaries via a three-player loss.
The loss includes the generated summary loss, the random summary loss, and the
real summary (ground-truth) loss, which play important roles for better
regularizing the learned model to obtain useful summaries. Comprehensive
experiments on three public datasets show the effectiveness of the proposed
approach.
