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- W2130482332 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 taskdependent cue to allow attention to stay mainly on one object in the scene each time for the first few shifts. Such a modification allows the learning of invariant object representations across attention shifts in a multiple-object scene. In this paper, we will first introduce this saliency map mechanism and then propose a neural network model to learn invariant representations for objects across attention shifts in a temporal sequence." @default.
- W2130482332 created "2016-06-24" @default.
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- W2130482332 date "2006-01-05" @default.
- W2130482332 modified "2023-09-25" @default.
- W2130482332 title "Selective Attention in the Learning of Invariant Representation of Objects" @default.
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- W2130482332 doi "https://doi.org/10.1109/cvpr.2005.522" @default.
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