This report describes our model for VATEX Captioning Challenge 2020. First, to gather information from multiple domains, we extract motion, appearance, semantic and audio features. Then we design a feature attention module to attend on different feature when decoding. We apply two types of decoders, top-down and X-LAN and ensemble these models to get the final result. The proposed method outperforms official baseline with a significant gap. We achieve 76.0 CIDEr and 50.0 CIDEr on English and Chinese private test set. We rank 2nd on both English and Chinese private test leaderboard.
@article{arxiv.2006.03315,
title = {Multi-modal Feature Fusion with Feature Attention for VATEX Captioning Challenge 2020},
author = {Ke Lin and Zhuoxin Gan and Liwei Wang},
journal= {arXiv preprint arXiv:2006.03315},
year = {2020}
}