Egocentric Video Task Translation @ Ego4D Challenge 2022
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 7 references that are in the map. First posted on arXiv 3 Feb 2023.
Builds on
6 of 7 references.
References · 6
- 2017Attention Is All You Needcited by 59,932
- 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,530
- 2018Taskonomy: Disentangling Task Transfer Learningcited by 631
- 2022Video + CLIP Baseline for Ego4D Long-term Action Anticipationcited by 18
- 2022Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022cited by 5
- 2022Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022cited by 3
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Citing papers per year: 2023: 2.
2 papers.