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
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ultra-fast multimodal and online transfer learning on humanoid robots
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Home robots, learn by themselves
UAHCI'13 Proceedings of the 7th international conference on Universal Access in Human-Computer Interaction: applications and services for quality of life - Volume Part III
Hi-index | 0.01 |
The problem of object classification when training and test classes are completely disjoint has recently become very popular in computer vision. To solve such problem, one needs to find common attributes of object and transfer them to use in classifying unseen object classes. Unfortunately, most recent attribute classifiers require the full batch-learning. This harshly prohibits an unseen-objects detection system from being used in online incremental machine such as robotics. This paper introduces a new approach for learning and classifying object's attribute in an online incremental manner. An approach is based on Self-Organizing and Incremental Neural Networks (SOINN). The evaluation has been done with 50-classes animal image dataset (30,000+ images). Comparing to the state-of-the-art method, our proposed approach named AT-SOINN (Attribute Transferring based on SOINN) performs the fast attribute learning, transferring and classification, while retaining high accuracy of attribute classification. Proposing AT-SOINN advances one more step towards online incremental unseen-object detections.