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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 9577
School of Cognitive Sciences
  Title:   Learning Object-based Attention Control
1.  Ali Borji
2.  Majid Nilli Ahmadabadi
3.  Babak Nadjar Araabi
  Status:   In Proceedings
  Proceeding: NIPS workshop on
  Year:  2008
  Supported by:  IPM
Remarkable efficiency of human vision is the main reason of being the most-studied mode of perception in machine learning. Despite the huge active research in computer vision and robotics, many real-world visumotor tasks that are easily performed by humans are still unsolved. Of special interest is designing efficient learning algorithms, in terms of high accuracy and low computational cost, for enabling autonomous mobile robots to act in visual interactive environments. Visual attention has been frequently used for reducing the complexity of computationally intensive processes. It solves the problem of information overloading by implementing a bottleneck through which only task-relevant information are allowed to pass. From a massive amount of studies in neuroscience and psychology, it is now known that visual attention is controlled by bottom-up and top-down mechanisms. Several theories for explaining the bottom-up influences of visual attention have been proposed like saliency concept, information theory, game theory, etc. While bottom-up mechanism is well understood, much less is known about the top-down component of visual attention.

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