Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/121617
Type: Journal article
Title: Generalised zero-shot learning with a classifier ensemble over multi-modal embedding spaces
Author: Felix, R.
Harwood, B.
Sasdelli, M.
Carneiro, G.
Citation: arXiv, 2019; OnlinePubl
Publisher: Cornell University
Issue Date: 2019
Statement of
Responsibility: 
Rafael Felix, Ben Harwood, Michele Sasdelli, Gustavo Carneiro
Abstract: Generalised zero-shot learning (GZSL) methods aim to classify previously seen and unseen visual classes by leveraging the semantic information of those classes. In the context of GZSL, semantic information is non-visual data such as a text description of both seen and unseen classes. Previous GZSL methods have utilised transformations between visual and semantic embedding spaces, as well as the learning of joint spaces that include both visual and semantic information. In either case, classification is then performed on a single learned space. We argue that each embedding space contains complementary information for the GZSL problem. By using just a visual, semantic or joint space some of this information will invariably be lost. In this paper, we demonstrate the advantages of our new GZSL method that combines classification of visual, semantic and joint spaces. Most importantly, this ensembling allows for more information from the source domains to be seen during classification. An additional contribution of our work is the application of a calibration procedure for each classifier in the ensemble. This calibration mitigates the problem of model selection when combining the classifiers. Lastly, our proposed method achieves state-of-the-art results on the CUB, AWA1 and AWA2 benchmark data sets and provides competitive performance on the SUN data set.
Rights: Copyright status unknown
RMID: 1000002024
Grant ID: http://purl.org/au-research/grants/arc/DP180103232
http://purl.org/au-research/grants/arc/CE140100016
http://purl.org/au-research/grants/arc/FL130100102
http://purl.org/au-research/grants/arc/LE190100080
Published version: https://arxiv.org/abs/1908.02013v1
Appears in Collections:Computer Science publications

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