Optimizing One-Shot Recognition with Micro-Set Learning

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 fea...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Kevin Tang, Marshall Tappen, Rahul Sukthankar, Christoph Lampert
• Keywords: Computer Vision, CVPR 2010, Internal Representation, Object Category Recognition, One-shot Recognition