574,035 research outputs found
A computational approach to the covert and overt deployment of spatial attention
Popular computational models of visual attention tend to neglect the
influence of saccadic eye movements whereas it has been shown that the primates
perform on average three of them per seconds and that the neural substrate for
the deployment of attention and the execution of an eye movement might
considerably overlap. Here we propose a computational model in which the
deployment of attention with or without a subsequent eye movement emerges from
local, distributed and numerical computations
Neurobiology, Psychophysics, and Computational Models of Visual Attention
The purpose of this workshop was to discuss both recent experimental findings and
computational models of the neurobiological implementation of selective attention.
Recent experimental results were presented in two of the four presentations given
(C.E. Connor, Washington University and B.C. Motter, SUNY and V.A. Medical
Center, Syracuse), while the other two talks were devoted to computational models
(E. Niebur, Caltech, and B. Olshausen, Washington University)
Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics
Economies are instances of complex socio-technical systems that are shaped by
the interactions of large numbers of individuals. The individual behavior and
decision-making of consumer agents is determined by complex psychological
dynamics that include their own assessment of present and future economic
conditions as well as those of others, potentially leading to feedback loops
that affect the macroscopic state of the economic system. We propose that the
large-scale interactions of a nation's citizens with its online resources can
reveal the complex dynamics of their collective psychology, including their
assessment of future system states. Here we introduce a behavioral index of
Chinese Consumer Confidence (C3I) that computationally relates large-scale
online search behavior recorded by Google Trends data to the macroscopic
variable of consumer confidence. Our results indicate that such computational
indices may reveal the components and complex dynamics of consumer psychology
as a collective socio-economic phenomenon, potentially leading to improved and
more refined economic forecasting.Comment: 21 pages, 6 figures, 13 table
Efficient Attention: Attention with Linear Complexities
Dot-product attention has wide applications in computer vision and natural
language processing. However, its memory and computational costs grow
quadratically with the input size. Such growth prohibits its application on
high-resolution inputs. To remedy this drawback, this paper proposes a novel
efficient attention mechanism equivalent to dot-product attention but with
substantially less memory and computational costs. Its resource efficiency
allows more widespread and flexible integration of attention modules into a
network, which leads to better accuracies. Empirical evaluations demonstrated
the effectiveness of its advantages. Efficient attention modules brought
significant performance boosts to object detectors and instance segmenters on
MS-COCO 2017. Further, the resource efficiency democratizes attention to
complex models, where high costs prohibit the use of dot-product attention. As
an exemplar, a model with efficient attention achieved state-of-the-art
accuracies for stereo depth estimation on the Scene Flow dataset. Code is
available at https://github.com/cmsflash/efficient-attention.Comment: To appear at WACV 202
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