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Multi-model learning by sequential reading of untrimmed videos for action recognition

arXiv 2401.14675 ↗First posted 26 Jan 2024cs.CVcited by 0

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Abstract (arXiv)

We propose a new method for learning videos by aggregating multiple models by sequentially extracting video clips from untrimmed video. The proposed method reduces the correlation between clips by feeding clips to multiple models in turn and synchronizes these models through federated learning. Experimental results show that the proposed method improves the performance compared to the no synchronization.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 26 Jan 2024.

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26 of 36 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2017The Kinetics Human Action Video Datasetcited by 1,894
  5. 2018SlowFast Networks for Video Recognitioncited by 1,862
  6. 2021ViViT: A Video Vision Transformercited by 1,610
  7. 2021Is Space-Time Attention All You Need for Video Understanding?cited by 1,609
  8. 2017The "something something" video database for learning and evaluating visual common sensecited by 1,092
  9. 2020X3D: Expanding Architectures for Efficient Video Recognitioncited by 635
  10. 2017Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?cited by 551
  11. 2017AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actionscited by 449
  12. 2021Federated Learning on Non-IID Data: A Surveycited by 345
14 more
  1. 2018Human Action Recognition and Prediction: A Surveycited by 226
  2. 2022MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognitioncited by 189
  3. 2021MoViNets: Mobile Video Networks for Efficient Video Recognitioncited by 135
  4. 2019Large-scale weakly-supervised pre-training for video action recognitioncited by 130
  5. 2020A Comprehensive Study of Deep Video Action Recognitioncited by 87
  6. 2021Token Shift Transformer for Video Classificationcited by 61
  7. 2020Omni-sourced Webly-supervised Learning for Video Recognitioncited by 48
  8. 2020High Performance I/O For Large Scale Deep Learningcited by 25
  9. 2022Vision Transformers for Action Recognition: A Surveycited by 25
  10. 2022Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistencycited by 15
  11. 2021Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memoriescited by 11
  12. 2021PGT: A Progressive Method for Training Models on Long Videoscited by 7
  13. 2019Spatio-temporal Action Recognition: A Surveycited by 5
  14. 2022Performance Evaluation of Action Recognition Models on Low Quality Videoscited by 3

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