  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'. Then, 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 first 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 effectiveness of our approach, we have created a new evaluation
dataset, which contains about 348 segments of scenes in 20 movie-trailers.
Through quantitative and qualitative evaluation, we show that our method is
effective for retrieval of video segments using natural language queries.
