VideoTRM: Pre-training for Video Captioning Challenge 2020
Jingwen Chen, Hongyang Chao · 2020
The Pre-training for Video Captioning Challenge 2020 mainly focuses on developing video captioning systems by pre-training on the newly released large-scale Auto-captions on GIF dataset and further transferring the pre-trained model to MSR-VTT benchmark. As a part of the submission to this challenge, we propose a Transformer based framework named VideoTRM, which consists of four modules: a textual encoder for encoding the linguistic relationship among words in the input sentence, a visual encoder for capturing the temporal dynamics in the input video, a cross-modal encoder for modeling the interactions between the two modalities (i.e., textual and visual) and a decoder for sentence generation conditioned on the input video and words generated previously. Additionally, we extend the decoder in our VideoTRM with mesh-like connections and gate fusion mechanism in multi-head attention during fine-tuning to take advantage of multi-level visual features and bypass less informative attention results, respectively. In the evaluation on test server, our VideoTRM achieves superior performances and ranks the second place on the leadboard finally.