Title
Optimizing One-Shot Recognition With Micro-Set Learning
Abstract
For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional class. One-shot recognition aims to apply the knowledge gained from a set of categories with plentiful data to categories for which only a single exemplar is available for each. As with earlier efforts motivated by transfer learning, we seek an internal representation for the domain that generalizes across classes. However, in contrast to existing work, we formulate the problem in a fundamentally new manner by optimizing the internal representation for the one-shot task using the notion of micro-sets. A micro-set is a sample of data that contains only a single instance of each category, sampled from the pool of available data, which serves as a mechanism to force the learned representation to explicitly address the variability and noise inherent in the one-shot recognition task. We optimize our learned domain features so that they minimize an expected loss over micro-sets drawn from the training set and show that these features generalize effectively to previously unseen categories. We detail a discriminative approach for optimizing one-shot recognition using micro-sets and present experiments on the Animals with Attributes and Caltech-101 datasets that demonstrate the benefits of our formulation. ©2010 IEEE.
Publication Date
8-31-2010
Publication Title
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Number of Pages
3027-3034
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/CVPR.2010.5540053
Copyright Status
Unknown
Socpus ID
77956008920 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/77956008920
STARS Citation
Tang, Kevin D.; Tappen, Marshall F.; Sukthankar, Rahul; and Lampert, Christoph H., "Optimizing One-Shot Recognition With Micro-Set Learning" (2010). Scopus Export 2010-2014. 1027.
https://stars.library.ucf.edu/scopus2010/1027