Few-Shot Video Classification via Temporal Alignment

@article{Cao2019FewShotVC,
  title={Few-Shot Video Classification via Temporal Alignment},
  author={Kaidi Cao and Jingwei Ji and Zhangjie Cao and C. Chang and Juan Carlos Niebles},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={10615-10624}
}
Difficulty in collecting and annotating large-scale video data raises a growing interest in learning models which can recognize novel classes with only a few training examples. In this paper, we propose the Ordered Temporal Alignment Module (OTAM), a novel few-shot learning framework that can learn to classify a previously unseen video. While most previous work neglects long-term temporal ordering information, our proposed model explicitly leverages the temporal ordering information in video… 

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