  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. 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 most other published methods by a large margin.

Computer Vision and Pattern Recognition;Neural and Evolutionary Computing