• Computer Science
  • Published in ArXiv 2017

In Defense of the Triplet Loss for Person Re-Identification

@article{Hermans2017InDO,
  title={In Defense of the Triplet Loss for Person Re-Identification},
  author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
  journal={ArXiv},
  year={2017},
  volume={abs/1703.07737}
}
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… CONTINUE READING

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