5,406 research outputs found
A computer vision model for visual-object-based attention and eye movements
This is the post-print version of the final paper published in Computer Vision and Image Understanding. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.This paper presents a new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach. Attention operates at multiple levels of visual selection by space, feature, object and group depending on the nature of targets and visual tasks. Attentional shifts and gaze shifts are constructed upon their common process circuits and control mechanisms but also separated from their different function roles, working together to fulfil flexible visual selection tasks in complicated visual environments. The framework integrates the important aspects of human visual attention and eye movements resulting in sophisticated performance in complicated natural scenes. The proposed approach aims at exploring a useful visual selection system for computer vision, especially for usage in cluttered natural visual environments.National Natural Science of Founda-
tion of Chin
Cognitive Deficit of Deep Learning in Numerosity
Subitizing, or the sense of small natural numbers, is an innate cognitive
function of humans and primates; it responds to visual stimuli prior to the
development of any symbolic skills, language or arithmetic. Given successes of
deep learning (DL) in tasks of visual intelligence and given the primitivity of
number sense, a tantalizing question is whether DL can comprehend numbers and
perform subitizing. But somewhat disappointingly, extensive experiments of the
type of cognitive psychology demonstrate that the examples-driven black box DL
cannot see through superficial variations in visual representations and distill
the abstract notion of natural number, a task that children perform with high
accuracy and confidence. The failure is apparently due to the learning method
not the CNN computational machinery itself. A recurrent neural network capable
of subitizing does exist, which we construct by encoding a mechanism of
mathematical morphology into the CNN convolutional kernels. Also, we
investigate, using subitizing as a test bed, the ways to aid the black box DL
by cognitive priors derived from human insight. Our findings are mixed and
interesting, pointing to both cognitive deficit of pure DL, and some measured
successes of boosting DL by predetermined cognitive implements. This case study
of DL in cognitive computing is meaningful for visual numerosity represents a
minimum level of human intelligence.Comment: Accepted for presentation at the AAAI-1
A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits
Developmental psychology and neuroimaging
research identified a close link between numbers and fingers,
which can boost the initial number knowledge in children. Recent
evidence shows that a simulation of the children's embodied
strategies can improve the machine intelligence too. This article
explores the application of embodied strategies to convolutional
neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually
acquired while operating rather than being abundant and fully
available as the classical machine learning scenarios. The
experimental analyses show that the proprioceptive information
from the robot fingers can improve network accuracy in the
recognition of handwritten Arabic digits when training examples
and epochs are few. This result is comparable to brain imaging
and longitudinal studies with young children. In conclusion, these
findings also support the relevance of the embodiment in the case
of artificial agents’ training and show a possible way for the
humanization of the learning process, where the robotic body can
express the internal processes of artificial intelligence making it
more understandable for humans
Prediction of intent in robotics and multi-agent systems.
Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches
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