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. That is while many problems in computer vision inherently have an underlying high-level structure and can benefit from it. Spatiotemporal graphs are a popular tool for imposing such high-level intuitions in the formulation of real world problems. In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and… CONTINUE READING

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