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.
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Li, M., Clark, J.J. (2007). Selective Attention in the Learning of Viewpoint and Position Invariance. In: Paletta, L., Rome, E. (eds) Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint. WAPCV 2007. Lecture Notes in Computer Science(), vol 4840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77343-6_17
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DOI: https://doi.org/10.1007/978-3-540-77343-6_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77342-9
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