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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Zero-Shot Recognition via Structured Prediction

verfasst von : Ziming Zhang, Venkatesh Saligrama

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

We develop a novel method for zero shot learning (ZSL) based on test-time adaptation of similarity functions learned using training data. Existing methods exclusively employ source-domain side information for recognizing unseen classes during test time. We show that for batch-mode applications, accuracy can be significantly improved by adapting these predictors to the observed test-time target-domain ensemble. We develop a novel structured prediction method for maximum a posteriori (MAP) estimation, where parameters account for test-time domain shift from what is predicted primarily using source domain information. We propose a Gaussian parameterization for the MAP problem and derive an efficient structure prediction algorithm. Empirically we test our method on four popular benchmark image datasets for ZSL, and show significant improvement over the state-of-the-art, on average, by 11.50 % and 30.12 % in terms of accuracy for recognition and mean average precision (mAP) for retrieval, respectively.

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Vorheriges Kapitel Network of Experts for Large-Scale Image Categorization
Nächstes Kapitel What’s the Point: Semantic Segmentation with Point Supervision
Fußnoten
1
For simplicity, in this paper we assume that there is only one cluster per class. With slight modification our method can also work in the cases where multiple clusters could correspond to one unseen class.
 
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Metadaten
Titel
Zero-Shot Recognition via Structured Prediction
verfasst von
Ziming Zhang
Venkatesh Saligrama
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46477-0

Electronic ISBN: 978-3-319-46478-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46478-7_33

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