Structural-RNN: Deep Learning on Spatio-Temporal Graphs

@article{Jain2016StructuralRNNDL,
  title={Structural-RNN: Deep Learning on Spatio-Temporal Graphs},
  author={Ashesh Jain and Amir Roshan Zamir and Silvio Savarese and Ashutosh Saxena},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={5308-5317}
}
Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. [] Key Method We develop a scalable method for casting an arbitrary spatio-temporal graph as a rich RNN mixture that is feedforward, fully differentiable, and jointly trainable. The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps. The evaluations of the…

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