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In Defense of the Triplet Loss for Person Re-Identification

Abstract · Mar 22, 2017 16:34 ·

cs-cv cs-ne

Arxiv Abstract

  • Alexander Hermans
  • Lucas Beyer
  • Bastian Leibe

In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person re-identification subfield is no exception to this, thanks to the notable publication of the Market-1501 and MARS datasets and several strong deep learning approaches. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms any other published method by a large margin.

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