Selective Attention in the Learning of Viewpoint and Position Invariance

  • Muhua Li
  • James J. Clark
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)


Selective attention plays an important role in visual processing in reducing the problem scale and in actively gathering useful information. We propose a modified saliency map mechanism that uses a simple top-down task-dependent cue to allow attention to stay mainly on one object in the scene each time for the first few shifts. Such a method allows the learning of invariant object representations across attention shifts in a multiple-object scene. In this paper, we construct a neural network that can learn position and viewpoint invariant representations for objects across attention shifts in a temporal sequence.


Body Motion Local Feature Selective Attention Sparse Code Attention Shift 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Muhua Li
    • 1
  • James J. Clark
    • 1
  1. 1.Centre for Intelligent Machines, McGill UniversityCanada

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