UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild. We introduce UCF101 which is currently the largest dataset of human actions. It consists of 101 action classes, over 13k clips and 27 hours of video data. The database consists of realistic user uploaded videos containing camera motion and cluttered background. Additionally, we provide baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%. To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.

References in zbMATH (referenced in 12 articles )

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  1. Haoqi Fan, Tullie Murrell, Heng Wang, Kalyan Vasudev Alwala, Yanghao Li, Yilei Li, Bo Xiong, Nikhila Ravi, Meng Li, Haichuan Yang, Jitendra Malik, Ross Girshick, Matt Feiszli, Aaron Adcock, Wan-Yen Lo, Christoph Feichtenhofer: PyTorchVideo: A Deep Learning Library for Video Understanding (2021) arXiv
  2. Ranjan, Anurag; Hoffmann, David T.; Tzionas, Dimitrios; Tang, Siyu; Romero, Javier; Black, Michael J.: Learning multi-human optical flow (2020)
  3. Zhao, Yue; Xiong, Yuanjun; Wang, Limin; Wu, Zhirong; Tang, Xiaoou; Lin, Dahua: Temporal action detection with structured segment networks (2020)
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  5. Lee, Sangseung; You, Donghyun: Data-driven prediction of unsteady flow over a circular cylinder using deep learning (2019)
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  7. Fernando, Basura; Gould, Stephen: Discriminatively learned hierarchical rank pooling networks (2017)
  8. Victor Campos, Brendan Jou, Xavier Giro-i-Nieto, Jordi Torres, Shih-Fu Chang: Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks (2017) arXiv
  9. Wang, Qian; Chen, Ke: Zero-shot visual recognition via bidirectional latent embedding (2017)
  10. Xu, Xun; Hospedales, Timothy; Gong, Shaogang: Transductive zero-shot action recognition by word-vector embedding (2017)
  11. E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, T. Brox: FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks (2016) arXiv
  12. Zhao, Kun; Alavi, Azadeh; Wiliem, Arnold; Lovell, Brian C.: Efficient clustering on Riemannian manifolds: a kernelised random projection approach (2016)