# Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022

arXiv: https://arxiv.org/abs/2211.15286 · HTML: https://mlsanity.com/paper/2211.15286/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 9 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Masked Autoencoders As Spatiotemporal Learners](https://mlsanity.com/paper/2205.09113/) (2022)
- [Egocentric Video-Language Pretraining @ Ego4D Challenge 2022](https://mlsanity.com/paper/2207.01622/) (2022)
- [Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022](https://mlsanity.com/paper/2207.11329/) (2022)
- [Object State Change Classification in Egocentric Videos using the Divided Space-Time Attention Mechanism](https://mlsanity.com/paper/2207.11814/) (2022)
- [Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022](https://mlsanity.com/paper/2206.07689/) (2022)

## Built on by (most cited first, 1 of 1)

- [Mistake Attribution: Fine-Grained Mistake Understanding in Egocentric Videos](https://mlsanity.com/paper/2511.20525/) (2025)

## 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.