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Learning a sequential search for landmarks
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015We propose a general method to find landmarks in images of objects using both appearance and spatial context. This method is applied without changes to two problems: parsing human body layouts, and finding landmarks in images of birds. Our method learns a sequential search for localizing landmarks, iteratively detecting new landmarks given the ...
Saurabh Singh 0005 +2 more
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Learning landmark triples by experimentation
Robotics and Autonomous Systems, 1997Abstract This article describes a method for learning a set of landmarks suitable for place navigation. The approach is novel in that it exploits the ability of a robot to learn through active perception in the task environment, similar to the learning by experimentation technique developed for LEX (Mitchell et al., 1990).
Robin R. Murphy +2 more
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Learning Deep Landmarks for Imbalanced Classification
IEEE Transactions on Neural Networks and Learning Systems, 2020We introduce a deep imbalanced learning framework called learning DEep Landmarks in laTent spAce (DELTA). Our work is inspired by the shallow imbalanced learning approaches to rebalance imbalanced samples before feeding them to train a discriminative classifier.
Feng Bao 0002 +5 more
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Landmarks in Route Learning by Girls and Boys
Perceptual and Motor Skills, 2000Previous research on route learning has shown that men learn routes faster and with fewer errors than women. The same patterns have also been found for girls and boys. In this study, 19 children of ages 5 to 6 years, 26 children of ages 7 to 9 years, and 22 children of ages 10 to 12 years were presented a route-learning task. The children were randomly
C D, Beilstein, J F, Wilson
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Landmark learning: An illustration of associative search
Biological Cybernetics, 1981In a previous paper we defined the associative search problem and presented a system capable of solving it under certain conditions. In this paper we interpret a spatial learning problem as an associative search task and describe the behavior of an adaptive network capable of solving it.
BARTO, AG, SUTTON, RS
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Learning visual landmarks for pose estimation
Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C), 2003We present an approach to vision-based mobile robot localization, even without an a-priori pose estimate. This is accomplished by learning a set of visual features called image-domain landmarks. The landmark learning mechanism is designed to be applicable to a wide range of environments.
Robert Sim, Gregory Dudek
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Learning Combinatorial Information from Alignments of Landmarks
Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007This paper characterizes the information space of a robot moving in the plane with limited sensing. The robot has a landmark detector, which provides the cyclic order of the landmarks around the robot, and it also has a touch sensor, that indicates when the robot is in contact with the environment boundary.
FREDA, Luigi +2 more
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Honeybees learn the colours of landmarks
Journal of Comparative Physiology A, 19861. To discover whether bees learn the colours of landmarks, individually marked foragers were trained to collect sucrose from a small reservoir on the floor of a room. The reservoir was placed at one of two sites each defined by its position relative to one of two different arrays of cylindrical landmarks.
K. Cheng, T. S. Collett, R. Wehner
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Route and landmark learning by rats searching for food
Behavioural Processes, 2011Many foraging animals rely on visual landmarks and/or habitual paths to locate important resources. We examined the degree to which rats rely on these cues when they predicted conflicting food locations. In this foraging task, rats were required to find food which could be located using either a fixed route or a nearby visual landmark.
Carolina, Tamara, William, Timberlake
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Multi-task Learning of Facial Landmarks and Expression
2014 Canadian Conference on Computer and Robot Vision, 2014Recently, deep neural networks have been shown to perform competitively on the task of predicting facial expression from images. Trained by gradient-based methods, these networks are amenable to "multi-task" learning via a multiple term objective. In this paper we demonstrate that learning representations to predict the position and shape of facial ...
Terrance Devries +2 more
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