MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation

@article{Farha2019MSTCNMT,
  title={MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation},
  author={Yazan Abu Farha and Juergen Gall},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={3570-3579}
}
Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics. [...] Key Method Each stage features a set of dilated temporal convolutions to generate an initial prediction that is refined by the next one. This architecture is trained using a combination of a classification loss and a proposed smoothing loss that penalizes over-segmentation errors. Extensive evaluation shows the effectiveness of the proposed model in…Expand
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