  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.
