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WAPCV
2004
Springer
15 years 3 months ago
Learning of Position-Invariant Object Representation Across Attention Shifts
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 ...
Muhua Li, James J. Clark
ICIP
1998
IEEE
15 years 11 months ago
Semantic Visual Templates: Linking Visual Features to Semantics
The rapid growth of visual data over the last few years has lead to many schemes for retrieving such data. With content-based systems today, there exists a significant gap between...
Shih-Fu Chang, William Chen, Hari Sundaram
TVCG
2002
99views more  TVCG 2002»
14 years 9 months ago
Lagrangian-Eulerian Advection of Noise and Dye Textures for Unsteady Flow Visualization
A new hybrid scheme (LEA) that combines the advantages of Eulerian and Lagrangian frameworks is applied to the visualization of dense representations of time-dependent vector field...
Bruno Jobard, Gordon Erlebacher, M. Yousuff Hussai...
NN
2006
Springer
234views Neural Networks» more  NN 2006»
14 years 9 months ago
Attention in natural scenes: Neurophysiological and computational bases
How does attention operate in natural scenes? We show that the receptive fields of inferior temporal cortex neurons that implement object representations become small and located ...
Edmund T. Rolls, Gustavo Deco
AUSAI
2008
Springer
14 years 12 months ago
Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling
In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neocortex and artificial intelligence models of mac...
John Thornton, Jolon Faichney, Michael Blumenstein...