Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Cross-Generalization: Learning Novel Classes from a Single Example by Feature Replacement
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
IEEE Transactions on Knowledge and Data Engineering
Efficient object category recognition using classemes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Multiple Kernel Learning for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Multiclass transfer learning from unconstrained priors
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 0.00 |
We present a transfer learning approach that transfers knowledge across two multi-class, unconstrained domains (source and target), and accomplishes object recognition with few training samples in the target domain. Unlike most of previous work, we make no assumption about the relatedness of these two domains. Namely, data of the two domains can be from different databases and of distinct categories. To overcome the domain variations, we propose to learn a set of commonly-shared and discriminant attributes in form of error-correcting output codes. Upon each of attributes, the unrelated, multi-class recognition tasks of the two domains are transformed into correlative, binary-class ones. The extra source knowledge can alleviate the high risk of overfitting caused by the lack of training data in the target domain. Our approach is evaluated on several benchmark datasets, and leads to about 40% relative improvement in accuracy when only one training sample is available.