In this paper, we describe the system for generating textual descriptions of
short video clips using recurrent neural networks (RNN), which we used while
participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015.
Our work builds on static image captioning systems with RNN based language
models and extends this framework to videos utilizing both static image
features and video-specific features. In addition, we study the usefulness of
visual content classifiers as a source of additional information for caption
generation. With experimental results we show that utilizing keyframe based
features, dense trajectory video features and content classifier outputs
together gives better performance than any one of them individually.

本文介绍了使用循环神经网络（RNN）生成短视频文本描述的系统，利用静态图像特征和视频专属特征的 RNN，我们研究了视觉内容分类器作为额外信息源的实用性，并通过实验结果证明了密集轨迹视频特征和内容分类器输出的利用，可以比单独利用它们表现更好。