Augmented attributes representations

Sharmanska, Viktoriia, Quadrianto, Novi and Lampert, Christoph H (2012) Augmented attributes representations. Published in: Proceedings of Computer vision - ECCV 2012: 12th European Conference on Computer Vision; Florence, Italy; 7-13 October 2012. 242-255. Springer Verlag ISBN 9783642337147

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We propose a new learning method to infer a mid-level feature representation that combines the advantage of semantic attribute representations with the higher expressive power of non-semantic features. The idea lies in augmenting an existing attribute-based representation with additional dimensions for which an autoencoder model is coupled with a large-margin principle. This construction allows a smooth transition between the zero-shot regime with no training example, the unsupervised regime with training examples but without class labels, and the supervised regime with training examples and with class labels. The resulting optimization problem can be solved efficiently, because several of the necessity steps have closed-form solutions. Through extensive experiments we show that the augmented representation achieves better results in terms of object categorization accuracy than the semantic representation alone.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > Q Science (General)
Depositing User: Novi Quadrianto
Date Deposited: 24 Feb 2014 14:26
Last Modified: 16 Jun 2017 13:38

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