Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning
Recently, deep learning approach, especially deep Convolutional Neural Networks (ConvNets) have achieved overwhelming accuracy with fast processing speed for image classification. Incorporating temporal structure with deep ConvNets for video representation becomes a fundamental problem for video content analysis. In this paper, we propose a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE) to exploit temporal information of videos. Compared to recent video representation inference approaches, this paper makes the following three contributions. First, our HRNE is able to efficiently exploit video temporal structure in a longer range by reducing the length of input information flow, and compositing multiple consecutive inputs at a higher level. Second, computation operations are significantly lessened while attaining more non linearity. Third, HRNE is able to uncover temporal tran sitions between frame chunks with different granularities, i.e. it can model the temporal transitions between frames as well as the transitions between segments. We apply the new method to video captioning where temporal information plays a crucial role. Experiments demonstrate that our method outperforms the state of the art on video captioning benchmarks.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)