- 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.
- That is while many problems in computer vision inherently have an underlying high-level structure and can benefit from it.
- We expect the method to empower new approaches to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks.
- Spatio-temporal graphs are a popular tool for imposing such high-level intuitions in the formulation of real world problems.
- 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.
Read the full article, click here.
@quantombone: “CVPR 2016 Best Student Paper: Structural-RNN: Deep Learning on Spatio-Temporal Graphs #cvpr”
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[1511.05298] Structural-RNN: Deep Learning on Spatio-Temporal Graphs