VideoSSL: Semi-Supervised Learning for Video Classification

@article{Jing2021VideoSSLSL,
  title={VideoSSL: Semi-Supervised Learning for Video Classification},
  author={Longlong Jing and Toufiq Parag and Zhe Wu and Yingli Tian and Hongcheng Wang},
  journal={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2021},
  pages={1109-1118}
}
We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised video classification methods demand a large amount of labeled data to attain good performance. However, annotation of a large dataset is expensive and time consuming. To minimize the dependence on a large annotated dataset, our proposed semi-supervised method trains from a small number of labeled examples and exploits… 
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