# Keyframe Segmentation and Positional Encoding for Video-guided Machine Translation Challenge 2020

arXiv: https://arxiv.org/abs/2006.12799 · HTML: https://mlsanity.com/paper/2006.12799/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine Translation](https://mlsanity.com/paper/2201.08054/) (2022)
- [BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation](https://mlsanity.com/paper/2305.18326/) (2023)
- [A Survey on Multi-modal Machine Translation: Tasks, Methods and Challenges](https://mlsanity.com/paper/2405.12669/) (2024)

## Abstract (arXiv)

Video-guided machine translation as one of multimodal neural machine translation tasks targeting on generating high-quality text translation by tangibly engaging both video and text. In this work, we presented our video-guided machine translation system in approaching the Video-guided Machine Translation Challenge 2020. This system employs keyframe-based video feature extractions along with the video feature positional encoding. In the evaluation phase, our system scored 36.60 corpus-level BLEU-4 and achieved the 1st place on the Video-guided Machine Translation Challenge 2020.