Learnable PINs: Cross-Modal Embeddings for Person Identity

Arsha Nagrani, Samuel Albanie, Andrew Zisserman; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 71-88


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.

Related Material

[pdf] [arXiv]
author = {Nagrani, Arsha and Albanie, Samuel and Zisserman, Andrew},
title = {Learnable PINs: Cross-Modal Embeddings for Person Identity},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}