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 of the Triplet Loss for Person ReIdentification REF .
In Defense of the Triplet Loss for Person Re-Identification
ArXiv
ArXiv
Computer Science