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Conference papers

Hard Negative Mining for Metric Learning Based Zero-Shot Classification

Maxime Bucher 1, 2 Stéphane Herbin 1 Frédéric Jurie 2
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : Zero-Shot learning has been shown to be an efficient strategy for domain adaptation. In this context, this paper builds on the recent work of Bucher et al. [1], which proposed an approach to solve Zero-Shot classification problems (ZSC) by introducing a novel metric learning based objective function. This objective function allows to learn an optimal embedding of the attributes jointly with a measure of similarity between images and attributes. This paper extends their approach by proposing several schemes to control the generation of the negative pairs, resulting in a significant improvement of the performance and giving above state-of-the-art results on three challenging ZSC datasets.
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Submitted on : Friday, August 26, 2016 - 1:42:00 PM
Last modification on : Wednesday, November 3, 2021 - 5:13:26 AM
Long-term archiving on: : Sunday, November 27, 2016 - 12:36:38 PM


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  • HAL Id : hal-01356757, version 1
  • ARXIV : 1608.07441


Maxime Bucher, Stéphane Herbin, Frédéric Jurie. Hard Negative Mining for Metric Learning Based Zero-Shot Classification. ECCV 16 WS TASK-CV: Transferring and Adapting Source Knowledge in Computer Vision, Oct 2016, Amsterdam, Netherlands. ⟨hal-01356757⟩



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