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Distance metric learning (DML) has been successfully applied to objectclassification, both in the standard regime of rich training data and in thefew-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 thebackbone network parameters, the embedding space, and the multi-modaldistribution of each of the training categories in that space, in a singleend-to-end training process. Our approach outperforms state-of-the-art methodsfor DML-based object classification on a variety of standard fine-graineddatasets. Furthermore, we demonstrate the effectiveness of our approach on theproblem of few-shot object detection, by incorporating the proposed DMLarchitecture as a classification head into a standard object detection model.We achieve the best results on the ImageNet-LOC dataset compared to strongbaselines, when only a few training examples are available. We also offer thecommunity a new episodic benchmark based on the ImageNet dataset for thefew-shot object detection task.

RepMet: Representative-based metric learning for classification and one-shot object detection