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Learnable PINs: Cross-modal Embeddings for Person Identity

  • Arsha NagraniEmail author
  • Samuel Albanie
  • Andrew Zisserman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11217)

Abstract

We propose and investigate an identity sensitive joint embedding of face and voice. Such an embedding enables cross-modal retrieval from voice to face and from face to voice.

We make the following four contributions: first, we show that the embedding can be learnt from videos of talking faces, without requiring any identity labels, using a form of cross-modal self-supervision; second, we develop a curriculum learning schedule for hard negative mining targeted to this task that is essential for learning to proceed successfully; third, we demonstrate and evaluate cross-modal retrieval for identities unseen and unheard during training over a number of scenarios and establish a benchmark for this novel task; finally, we show an application of using the joint embedding for automatically retrieving and labelling characters in TV dramas.

Keywords

Joint embedding Cross-modal Multi-modal Self-supervised Face recognition Speaker identification Metric learning 

Notes

Acknowledgements

The authors gratefully acknowledge the support of EPSRC CDT AIMS grant EP/L015897/1 and the Programme Grant Seebibyte EP/M013774/1. The authors would also like to thank Judith Albanie for helpful suggestions.

Supplementary material

474201_1_En_5_MOESM1_ESM.pdf (206 kb)
Supplementary material 1 (pdf 205 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.VGG, Department of Engineering ScienceOxfordUK

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