Structural-RNN: Deep Learning on Spatio-Temporal Graphs

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. Spatio-temporal 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 sequence learning success of Recurrent Neural Networks~(RNNs). 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 proposed approach on a diverse set of problems, ranging from modeling human motion to object interactions, shows improvement over the state-of-the-art with a large margin. We expect this method to empower new approaches to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Skeleton Based Action Recognition CAD-120 S-RNN (5-shot) Accuracy 85.4% # 4
Human Pose Forecasting Human3.6M SRNN MAR, walking, 400ms 1.30 # 6
MAR, walking, 1,000ms 2.13 # 6

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