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.

通过同时训练具有深度网络的图像表示和相应的度量，在看不见的类和示例的表示学习中取得了最新的突破，从而在深度度量学习中取得了进步。最近的贡献主要涉及训练部分（损失函数，采样策略等），而一些工作着重于提高图像表示的判别力。在本文中，我们提出了DIABLO，一种基于字典的图像嵌入注意方法。戴铂通过仅将与视觉相关的特征聚合在一起，可以生成更丰富的表示，同时比深度度量学习中其他基于注意的方法更易于训练。实验在四个深度度量学习数据集（Cub-200-2011，Cars-196，斯坦福在线产品和店内衣服检索）上得到了实验证实，DIABLO为其展示了最先进的性能。