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» Transferring Visual Category Models to New Domains
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AAAI
2008
14 years 12 months ago
Transfer Learning via Dimensionality Reduction
Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the train...
Sinno Jialin Pan, James T. Kwok, Qiang Yang
VIS
2003
IEEE
169views Visualization» more  VIS 2003»
15 years 11 months ago
Gaussian Transfer Functions for Multi-Field Volume Visualization
Volume rendering is a flexible technique for visualizing dense 3D volumetric datasets. A central element of volume rendering is the conversion between data values and observable q...
Aaron E. Lefohn, Charles D. Hansen, Emil Praun, Jo...
VISUALIZATION
2003
IEEE
15 years 2 months ago
Visually Accurate Multi-Field Weather Visualization
Weather visualization is a difficult problem because it comprises volumetric multi-field data and traditional surface-based approaches obscure details of the complex three-dimen...
Kirk Riley, David S. Ebert, Charles D. Hansen, Jas...
VIS
2006
IEEE
117views Visualization» more  VIS 2006»
15 years 11 months ago
Transfer Function Fusing
Based on the observation that it is relatively easier for users to generate several good transfer functions (TFs) for different features of volumetric data, we propose TF fusing, ...
Yingcai Wu, Huamin Qu, Hong Zhou, Ming-Yuen Cha...
ICCV
2003
IEEE
15 years 11 months ago
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires mode...
Fei-Fei Li 0002, Robert Fergus, Pietro Perona