Learning From a Small Number of Training Examples by Exploiting Object Categories
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
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
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructing informative priors using transfer learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic attribute discovery and characterization from noisy web data
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A discriminative latent model of object classes and attributes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Evaluating knowledge transfer and zero-shot learning in a large-scale setting
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning to share visual appearance for multiclass object detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Tabula rasa: Model transfer for object category detection
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Transfer learning can counter the heavy-tailed nature of the distribution of training examples over object classes. Here, we study transfer learning for object class detection. Starting from the intuition that "what makes a good detector" should manifest itself in the form of repeatable statistics over existing "good" detectors, we design a low-level feature model that can be used as a prior for learning new object class models from scarce training data. Our priors are structured, capturing dependencies both on the level of individual features and spatially neighboring pairs of features. We confirm experimentally the connection between the information captured by our priors and "good" detectors as well as the connection to transfer learning from sources of different quality. We give an in-depth analysis of our priors on a subset of the challenging PASCAL VOC 2007 data set and demonstrate improved average performance over all 20 classes, achieved without manual intervention.