Selective Attention in the Learning of Viewpoint and Position Invariance

Abstract

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.KeywordsBody MotionLocal FeatureSelective AttentionSparse CodeAttention ShiftThese 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.