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Convolutional Learning of Spatio-temporal Features

  • Graham W. Taylor
  • Rob Fergus
  • Yann LeCun
  • Christoph Bregler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)

Abstract

We address the problem of learning good features for understanding video data. We introduce a model that learns latent representations of image sequences from pairs of successive images. The convolutional architecture of our model allows it to scale to realistic image sizes whilst using a compact parametrization. In experiments on the NORB dataset, we show our model extracts latent “flow fields” which correspond to the transformation between the pair of input frames. We also use our model to extract low-level motion features in a multi-stage architecture for action recognition, demonstrating competitive performance on both the KTH and Hollywood2 datasets.

Keywords

Action Recognition Interest Point Image Patch Sparse Code Human Activity Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Supplementary material

978-3-642-15567-3_11_MOESM1_ESM.pdf (168 kb)
Electronic Supplementary Material (169 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Graham W. Taylor
    • 1
  • Rob Fergus
    • 1
  • Yann LeCun
    • 1
  • Christoph Bregler
    • 1
  1. 1.Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA

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