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ECCV
2008
Springer
14 years 7 months ago
Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks
Abstract. Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchi...
Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, Eric P. Xi...
NECO
2007
127views more  NECO 2007»
13 years 5 months ago
Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...
Joseph F. Murray, Kenneth Kreutz-Delgado
CVPR
2011
IEEE
13 years 1 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
APGV
2004
ACM
176views Visualization» more  APGV 2004»
13 years 11 months ago
Towards perceptually realistic talking heads: models, methods and McGurk
Motivated by the need for an informative, unbiased and quantitative perceptual method for the development and evaluation of a talking head we are developing, we propose a new test...
Darren Cosker, Susan Paddock, A. David Marshall, P...