mlsanityData to 7 Oct 2026

Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022

arXiv 2207.11329 ↗First posted 22 Jul 2022cs.CVcited by 5

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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.

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

Builds on

7 of 11 references.

References · 7

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  2. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  3. 2017The Kinetics Human Action Video Datasetcited by 1,894
  4. 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,530
  5. 2021Video Swin Transformercited by 1,090
  6. 2021Multiscale Vision Transformerscited by 896
  7. 2018A Short Note about Kinetics-600cited by 331

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 1.

5 papers.

  1. 2024EgoLM: Multi-Modal Language Model of Egocentric Motionscited by 28
  2. 2022Egocentric Video Task Translationcited by 19
  3. 2022EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokenscited by 15
  4. 2023Egocentric Video Task Translation @ Ego4D Challenge 2022cited by 2
  5. 2022Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022cited by 1