  Existing video summarization approaches mainly concentrate on sequential or
structural characteristic of video data. However, they do not pay enough
attention to the video summarization task itself. In this paper, we propose a
meta learning method for performing task-driven video summarization, denoted by
MetaL-TDVS, to explicitly explore the video summarization mechanism among
summarizing processes on different videos. Particularly, MetaL-TDVS aims to
excavate the latent mechanism for summarizing video by reformulating video
summarization as a meta learning problem and promote generalization ability of
the trained model. MetaL-TDVS regards summarizing each video as a single task
to make better use of the experience and knowledge learned from processes of
summarizing other videos to summarize new ones. Furthermore, MetaL-TDVS updates
models via a two-fold back propagation which forces the model optimized on one
video to obtain high accuracy on another video in every training step.
Extensive experiments on benchmark datasets demonstrate the superiority and
better generalization ability of MetaL-TDVS against several state-of-the-art
methods.
