Working hard to know your neighbor's margins: Local descriptor learning loss

Autoren Anastasiya Mishchuk
Dmytro Mishkin
Filip Radenovic
Jiri Matas
Editoren
Titel Working hard to know your neighbor's margins: Local descriptor learning loss
Buchtitel Advances in Neural Information Processing Systems 30 - Proc. NIPS 2017
Typ in Konferenzband
Monat December
Jahr 2017
Seiten online
SCCH ID# 17088
Abstract

We introduce a loss for metric learning, which is inspired by the Lowe’s matching criterion for SIFT. We show that the proposed loss, that maximizes the distance between the closest positive and closest negative example in the batch, is better than complex regularization methods; it works well for both shallow and deep convolution network architectures. Applying the novel loss to the L2Net CNN architecture results in a compact descriptor named HardNet. It has the same dimensionality as SIFT (128) and shows state-of-art performance in wide baseline stereo, patch verification and instance retrieval benchmarks.