Egocentric Video Task Translation @ Ego4D Challenge 2022

Autor:	Xue, Zihui, Song, Yale, Grauman, Kristen, Torresani, Lorenzo
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	This technical report describes the EgoTask Translation approach that explores relations among a set of egocentric video tasks in the Ego4D challenge. To improve the primary task of interest, we propose to leverage existing models developed for other related tasks and design a task translator that learns to ''translate'' auxiliary task features to the primary task. With no modification to the baseline architectures, our proposed approach achieves competitive performance on two Ego4D challenges, ranking the 1st in the talking to me challenge and the 3rd in the PNR keyframe localization challenge. Comment: The technical report of ECCV@2022 Ego4D challenge
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.01891 Zobrazit plný text záznamu View this record from Arxiv