HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
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.
Complete list of metadata

Cited literature [18 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01356757
Contributor : Maxime Bucher Connect in order to contact the contributor
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

Files

1-paper.pdf
Files produced by the author(s)

Identifiers

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

Citation

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⟩

Share

Metrics

Record views

403

Files downloads

636