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In this paper, we propose a new approach for retrieval of video segmentsusing natural language queries. Unlike most previous approaches such asconcept-based methods or rule-based structured models, the proposed method usesimage captioning model to construct sentential queries for visual information.In detail, our approach exploits multiple captions generated by visual featuresin each image with `Densecap'. Then, the similarities between captions ofadjacent images are calculated, which is used to track semantically similarcaptions over multiple frames. Besides introducing this novel idea of 'trackingby captioning', the proposed method is one of the first approaches that uses alanguage generation model learned by neural networks to construct semanticquery describing the relations and properties of visual information. Toevaluate the effectiveness of our approach, we have created a new evaluationdataset, which contains about 348 segments of scenes in 20 movie-trailers.Through quantitative and qualitative evaluation, we show that our method iseffective for retrieval of video segments using natural language queries.

Where to Play: Retrieval of Video Segments using Natural-Language Queries