RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection

Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, Alex M. Bronstein; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5197-5206

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Karlinsky_2019_CVPR,
author = {Karlinsky, Leonid and Shtok, Joseph and Harary, Sivan and Schwartz, Eli and Aides, Amit and Feris, Rogerio and Giryes, Raja and Bronstein, Alex M.},
title = {RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}