  This paper addresses the problem of video summarization. Given an input
video, the goal is to select a subset of the frames to create a summary video
that optimally captures the important information of the input video. With the
large amount of videos available online, video summarization provides a useful
tool that assists video search, retrieval, browsing, etc. In this paper, we
formulate video summarization as a sequence labeling problem. Unlike existing
approaches that use recurrent models, we propose fully convolutional sequence
models to solve video summarization. We firstly establish a novel connection
between semantic segmentation and video summarization, and then adapt popular
semantic segmentation networks for video summarization. Extensive experiments
and analysis on two benchmark datasets demonstrate the effectiveness of our
models.
