Mar 26, 2015 · Our zero-shot learning experiments on a number of standard multi-label datasets demonstrate that our method outperforms a variety of baselines.
In this paper, a novel framework for multi-label zero-shot learning is proposed. Our framework is based on transfer learning – given a train- ing/auxiliary ...
In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: ...
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Zero-shot learning has received increasing interest as a means to alleviate the prohibitive expense of annotating training data for large scale recog-.
Mar 26, 2015 · In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learn- ing, addressing the unique ...
In this paper we discuss two re- lated lines of work improving the conventional approach: exploiting transductive learning ZSL, and generalising ZSL to the ...
This paper proposes a multi-output deep regression model to project an image into a semantic word space, which explicitly exploits the correlations in the ...
Jul 24, 2014 · Zero-shot learning aims to classify visual objects without any training data via knowledge transfer between seen and unseen classes. This is ...
To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive ...
Mar 3, 2015 · Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an ...