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Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification

Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification

This video was recorded at British Machine Vision Conference (BMVC), Surrey 2012. We present in this paper a novel learning-based approach for video sequence classification. Contrary to the dominant methodology, which relies on hand-crafted features that are manually engineered to be optimal for a specific task, our neural model automatically learns a sparse shift-invariant representation of the local 2D+t salient information, without any use of prior knowledge. To that aim, a spatio-temporal convolutional sparse autoencoder is trained to project a given input in a feature space, and to reconstruct it from its projection coordinates. Learning is performed in an unsupervised manner by minimizing a global parametrized objective function. The sparsity is ensured by adding a sparsifying... Show More

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