Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. In this work, we propose a new method for DML that simultaneously learns the backbone network parameters, the embedding space, and the multi-modal distribution of each of the training categories in that space, in a single end-to-end training process. Our approach outperforms state-of-the-art methods for DML-based object classification on a variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness of our approach on the problem of few-shot object detection, by incorporating the proposed DML architecture as a classification head into a standard object detection model. We achieve the best results on the ImageNet-LOC dataset compared to strong baselines, when only a few training examples are available. We also offer the community a new episodic benchmark based on the ImageNet dataset for the few-shot object detection task.
@article{arxiv.1806.04728,
title = {RepMet: Representative-based metric learning for classification and one-shot object detection},
author = {Leonid Karlinsky and Joseph Shtok and Sivan Harary and Eli Schwartz and Amit Aides and Rogerio Feris and Raja Giryes and Alex M. Bronstein},
journal= {arXiv preprint arXiv:1806.04728},
year = {2018}
}