# Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022

arXiv: https://arxiv.org/abs/2207.11329 · HTML: https://mlsanity.com/paper/2207.11329/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [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)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [A Short Note about Kinetics-600](https://mlsanity.com/paper/1808.01340/) (2018)

## Built on by (most cited first, 5 of 5)

- [EgoLM: Multi-Modal Language Model of Egocentric Motions](https://mlsanity.com/paper/2409.18127/) (2024)
- [Egocentric Video Task Translation](https://mlsanity.com/paper/2212.06301/) (2022)
- [EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens](https://mlsanity.com/paper/2211.10636/) (2022)
- [Egocentric Video Task Translation @ Ego4D Challenge 2022](https://mlsanity.com/paper/2302.01891/) (2023)
- [Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022](https://mlsanity.com/paper/2211.15286/) (2022)

## Abstract (arXiv)

We implemented Video Swin Transformer as a base architecture for the tasks of Point-of-No-Return temporal localization and Object State Change Classification. Our method achieved competitive performance on both challenges.