Learning of position-invariant object representation across attention shifts

Authors:
Muhua Li;James J. Clark
Affiliations:
Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada;Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada
Venue:
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Year:
2004

Citing 5
Cited 1

Learning invariance from transformation sequences

Neural Computation
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Implicit learning in 3D object recognition: the importance of temporal context

Neural Computation
A reinforcement learning model of selective visual attention

Proceedings of the fifth international conference on Autonomous agents
Neurons with Two Sites of Synaptic Integration Learn Invariant Representations

Neural Computation

Selective Attention in the Learning of Viewpoint and Position Invariance

Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint

Quantified Score

Hi-index	0.00

Visualization

Abstract

Selective attention shift can help neural networks learn invariance. We describe a method that can produce a network with invariance to changes in visual input caused by attention shifts. Training of the network is controlled by signals associated with attention shifting. A temporal perceptual stability constraint is used to drive the output of the network towards remaining constant across temporal sequences of attention shifts. We use a four-layer neural network model to perform the position-invariant extraction of local features and temporal integration of attention-shift invariant presentations of objects. We present results on both simulated data and real images, to demonstrate that our network can acquire position invariance across a sequence of attention shifts.