Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
Not on the map. Why?
Abstract (arXiv)
In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022. As team TheSSVL, we ranked 2nd place in both tasks. Our code will be made available.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 9 references that are in the map. First posted on arXiv 18 Nov 2022.
Builds on
9 of 9 references.
References · 9
- 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
- 2017The Kinetics Human Action Video Datasetcited by 1,894
- 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,530
- 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,305
- 2022Masked Autoencoders As Spatiotemporal Learnerscited by 428
- 2022Egocentric Video-Language Pretraining @ Ego4D Challenge 2022cited by 6
- 2022Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022cited by 5
- 2022Object State Change Classification in Egocentric Videos using the Divided Space-Time Attention Mechanismcited by 4
- 2022Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022cited by 3
Built on by
Citing papers per year: 2025: 1.
1 paper.