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Communication Dans Un Congrès Année : 2016

Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification

Résumé

This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images – one of the main ingredients of zero-shot learning – by formulating it as a metric learning problem. The optimized empirical criterion associates two types of sub-task constraints: metric discriminating capacity and accurate attribute prediction. This results in a novel expression of zero-shot learning not requiring the notion of class in the training phase: only pairs of image/attributes, augmented with a consistency indicator, are given as ground truth. At test time, the learned model can predict the consistency of a test image with a given set of attributes , allowing flexible ways to produce recognition inferences. Despite its simplicity, the proposed approach gives state-of-the-art results on four challenging datasets used for zero-shot recognition evaluation.
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Dates et versions

hal-01348827 , version 1 (25-07-2016)

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Maxime Bucher, Stéphane Herbin, Frédéric Jurie. Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification. ECCV 2016, Oct 2016, amsterdam, Netherlands. ⟨hal-01348827⟩
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