Abstract. We present an efficient framework that can generate a coherent paragraph to describe a given video. Previous works on video captioning usually focus on video clips. They typically treat an entire video as a whole and generate the caption conditioned on a single embedding. On the contrary, we consider videos with rich temporal structures and aim to generate paragraph descriptions that can preserve the story flow while being coherent and concise. Towards this goal, we propose a new approach, which produces a descriptive paragraph by assembling temporally localized descriptions. Given a video, it selects a sequence of distinctive clips and generates sentences thereon in a coherent manner. Particularly, the selection of clips and the production of sentences are done jointly and progressively driven by a recurrent network -what to describe next depends on what have been said before. Here, the recurrent network is learned via self-critical sequence training with both sentencelevel and paragraph-level rewards. On the ActivityNet Captions dataset, our method demonstrated the capability of generating high-quality paragraph descriptions for videos. Compared to those by other methods, the descriptions produced by our method are often more relevant, more coherent, and more concise.
REF produces descriptive paragraphs for videos via a recurrent network by assembling temporally localized descriptions.
Move Forward and Tell: A Progressive Generator of Video Descriptions
European Conference on Computer Vision (ECCV), 2018
Computer Science