# Multi-modal Feature Fusion with Feature Attention for VATEX Captioning Challenge 2020

arXiv: https://arxiv.org/abs/2006.03315 · HTML: https://mlsanity.com/paper/2006.03315/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Review of the Video-to-Text Problem](https://mlsanity.com/paper/2103.14785/) (2021)
- [Synchronized Audio-Visual Frames with Fractional Positional Encoding for Transformers in Video-to-Text Translation](https://mlsanity.com/paper/2112.14088/) (2021)

## Abstract (arXiv)

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.