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

In this paper, we propose a new approach for retrieval of video segments using natural language queries. Unlike most previous approaches such as concept-based methods or rule-based structured models, the proposed method uses image captioning model to construct sentential queries for visual information. In detail, our approach exploits multiple captions generated by visual features in each image with ‘Densecap’. Œen, the similarities between captions of adjacent images are calculated, which is used to track semantically similar captions over multiple frames. Besides introducing this novel idea of ’tracking by captioning’, the proposed method is one of the €rst approaches that uses a language generation model learned by neural networks to construct semantic query describing the relations and properties of visual information. To evaluate the e‚ectiveness of our approach, we have created a new evaluation dataset, which contains about 348 segments of scenes in 20 movie-trailers. Œrough quantitative and qualitative evaluation, we show that our method is e‚ective for retrieval of video segments using natural language queries.