Understanding temporal structure for video captioning

Recent research in convolutional and recurrent neural networks has fueled incredible advances in video understanding. We propose a video captioning framework that achieves the performance and quality necessary to be deployed in distributed surveillance systems. Our method combines an efficient hierarchical architecture with novel attention mechanisms at both the local and global levels. By shifting focus to different spatiotemporal locations, attention mechanisms correlate sequential outputs with activation maps, offering a clever way to adaptively combine multiple frames and locations of video. As soft attention mixing weights are solved via back-propagation, the number of weights or input frames needs to be known in advance. To remove this restriction, our video understanding framework combines continuous attention mechanisms over a family of Gaussian distributions. Our efficient multistream hierarchical model combines a recurrent architecture with a soft hierarchy layer using both equally spaced and dynamically localized boundary cuts. As opposed to costly volumetric attention approaches, we use video attributes to steer temporal attention. Our fully learnable end-to-end approach helps predict salient temporal regions of action/objects in the video. We demonstrate state-of-the-art captioning results on the popular MSVD, MSR-VTT and M-VAD video datasets and compare several variants of the algorithm suitable for real-time applications. By adjusting the frame rate, we show a single computer can generate effective video captions for 100 simultaneous cameras. We additionally perform studies to show how bit rate compression modifies captioning results.