  A generic video summary is an abridged version of a video that conveys the
whole story and features the most important scenes. Yet the importance of
scenes in a video is often subjective, and users should have the option of
customizing the summary by using natural language to specify what is important
to them. Further, existing models for fully automatic generic summarization
have not exploited available language models, which can serve as an effective
prior for saliency. This work introduces CLIP-It, a single framework for
addressing both generic and query-focused video summarization, typically
approached separately in the literature. We propose a language-guided
multimodal transformer that learns to score frames in a video based on their
importance relative to one another and their correlation with a user-defined
query (for query-focused summarization) or an automatically generated dense
video caption (for generic video summarization). Our model can be extended to
the unsupervised setting by training without ground-truth supervision. We
outperform baselines and prior work by a significant margin on both standard
video summarization datasets (TVSum and SumMe) and a query-focused video
summarization dataset (QFVS). Particularly, we achieve large improvements in
the transfer setting, attesting to our method's strong generalization
capabilities.
