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Saliency Based on Information Maximization
TLDR
A model of bottom-up overt attention is proposed based on the principle of maximizing information sampled from a scene and is achieved in a neural circuit, which is demonstrated as having close ties with the circuitry existent in die primate visual cortex. Expand
Saliency, attention, and visual search: an information theoretic approach.
TLDR
It is demonstrated that a variety of visual search behaviors appear as emergent properties of the model and therefore basic principles of coding and information transmission are demonstrated. Expand
Modeling Visual Attention via Selective Tuning
TLDR
This model is a hypothesis for primate visual attention, but it also outperforms existing computational solutions for attention in machine vision and is highly appropriate to solving the problem in a robot vision system. Expand
Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI
TLDR
A 44-fold increase in fitting speed and a segmentation accuracy that is on par with Gauss-Newton optimization, one of the most widely used optimization algorithms for such problems. Expand
Analyzing vision at the complexity level
TLDR
This analysis of visual search performance in terms of attentional influences on visual information processing and complexity satisfaction allows a large body of neurophysiological and psychological evidence to be tied together. Expand
A Computational Perspective on Visual Attention
TLDR
The Selective Tuning model of vision and attention, developed by John Tsotsos, explains attentive behavior in humans and provides a foundation for building computer systems that see with human-like characteristics. Expand
Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior
TLDR
A novel dataset is introduced which in addition to providing the bounding box information for pedestrian detection, also includes the behavioral and contextual annotations for the scenes, which allows combining visual and semantic information for better understanding of pedestrians' intentions in various traffic scenarios. Expand
Incremental Learning Through Deep Adaptation
TLDR
This work proposes a method called Deep Adaptation Modules (DAM) that constrains newly learned filters to be linear combinations of existing ones, and reduces the parameter cost to around 3 percent of the original with negligible or no loss in accuracy. Expand
The selective tuning model of attention: psychophysical evidence for a suppressive annulus around an attended item
TLDR
Mapped the attentional field around an attended location in a matching task where the subject's attention was directed to a cued target while the distance of a probe item to the target was varied systematically, and found that accuracy increased with inter-target separation. Expand
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