Multirate Multimodal Video Captioning

Automatically describing videos with natural language is a crucial challenge of video understanding. Compared to images, videos have specific spatial-temporal structure and various modality information. In this paper, we propose a Multirate Multimodal Approach for video captioning. Considering that the speed of motion in videos varies constantly, we utilize a Multirate GRU to capture temporal structure of videos. It encodes video frames with different intervals and has a strong ability to deal with motion speed variance. As videos contain different modality cues, we design a particular multimodal fusion method. By incorporating visual, motion, and topic information together, we construct a well-designed video representation. Then the video representation is fed into a RNN-based language model for generating natural language descriptions. We evaluate our approach for video captioning on "Microsoft Research-Video to Text" (MSR-VTT), a large-scale video benchmark for video understanding. And our approach gets great performance on the 2nd MSR Video to Language Challenge.