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The SkatingVerse Workshop & Challenge: Methods and Results

arXiv 2405.17188 ↗First posted 27 May 2024cs.CVcited by 0

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Abstract (arXiv)

The SkatingVerse Workshop & Challenge aims to encourage research in developing novel and accurate methods for human action understanding. The SkatingVerse dataset used for the SkatingVerse Challenge has been publicly released. There are two subsets in the dataset, i.e., the training subset and testing subset. The training subsets consists of 19,993 RGB video sequences, and the testing subsets consists of 8,586 RGB video sequences. Around 10 participating teams from the globe competed in the SkatingVerse Challenge. In this paper, we provide a brief summary of the SkatingVerse Workshop & Challenge including brief introductions to the top three methods. The submission leaderboard will be reopened for researchers that are interested in the human action understanding challenge. The benchmark dataset and other information can be found at: https://skatingverse.github.io/.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 14 references that are in the map. First posted on arXiv 27 May 2024.

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References · 12

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
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  4. 2017The Kinetics Human Action Video Datasetcited by 1,909
  5. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,555
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  7. 2023VideoMAE V2: Scaling Video Masked Autoencoders with Dual Maskingcited by 528
  8. 2022ViTPose: Simple Vision Transformer Baselines for Human Pose Estimationcited by 456
  9. 2022UniFormer: Unifying Convolution and Self-attention for Visual Recognitioncited by 206
  10. 2023Unmasked Teacher: Towards Training-Efficient Video Foundation Modelscited by 199
  11. 2022UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormercited by 116
  12. 2022Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learningcited by 90

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