Work

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

Year: 2020

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2006.12799

Authors: Hirasawa, Tosho, Yang, Zhishen, Komachi, Mamoru, Okazaki, Naoaki

Institutions: Tokyo Metropolitan University, Tokyo Institute of Technology

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3036286589

Cites: 10

Cited by: 8

Related to: 10