Cross-Generalization: Learning Novel Classes from a Single Example by Feature Replacement

We develop an object classification method that can learn a novel class from a single training example. In this method, experience with already learned classes is used to facilitate the learning of novel classes. Our classification scheme employs features that discriminate between class and non-class images. For a novel class, new features are derived by selecting features that proved useful for already learned classification tasks, and adapting these features to the new classification task. This adaptation is performed by replacing the features from already learned classes with similar features taken from the novel class. A single example of a novel class is sufficient to perform feature adaptation and achieve useful classification performance. Experiments demonstrate that the proposed algorithm can learn a novel class from a single training example, using 10 additional familiar classes. The performance is significantly improved compared to using no feature adaptation. The robustness...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Evgeniy Bart, Shimon Ullman
• Keywords: Computer Vision, CVPR 2005, Feature Adaptation, Feature Adaptation Concept, Object Classification Method, Single Training Example, Useful Classification Performance