  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.
