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 reidentification 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. * Equal contribution. Ordering determined by a last minute coin flip. 1 A nice overview of the field is given by a recent survey paper [40] .
In defense REF made use of Triplet Loss to show that it can be used to perform end to end deep metric learning.
In Defense of the Triplet Loss for Person Re-Identification
ArXiv
ArXiv
Computer Science