# Egocentric Video Task Translation @ Ego4D Challenge 2022

arXiv: https://arxiv.org/abs/2302.01891 · HTML: https://mlsanity.com/paper/2302.01891/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [Video + CLIP Baseline for Ego4D Long-term Action Anticipation](https://mlsanity.com/paper/2207.00579/) (2022)
- [Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022](https://mlsanity.com/paper/2207.11329/) (2022)
- [Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022](https://mlsanity.com/paper/2206.07689/) (2022)

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

- [The Audio-Visual Conversational Graph: From an Egocentric-Exocentric Perspective](https://mlsanity.com/paper/2312.12870/) (2023)
- [QuAVF: Quality-aware Audio-Visual Fusion for Ego4D Talking to Me Challenge](https://mlsanity.com/paper/2306.17404/) (2023)

## Abstract (arXiv)

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.