Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on improving the discriminative power of the image representation. In this paper, we propose DIABLO, a dictionary-based attention method for image embedding. DIABLO produces richer representations by aggregating only visually-related features together while being easier to train than other attention-based methods in deep metric learning. This is experimentally confirmed on four deep metric learning datasets (Cub-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval) for which DIABLO shows state-of-the-art performances.
@article{arxiv.2004.14644,
title = {DIABLO: Dictionary-based Attention Block for Deep Metric Learning},
author = {Pierre Jacob and David Picard and Aymeric Histace and Edouard Klein},
journal= {arXiv preprint arXiv:2004.14644},
year = {2020}
}
Comments
Pre-print. Accepted for publication at Pattern Recognition Letters