We propose a novel approach for instance-level image retrieval. It produces a
global and compact fixed-length representation for each image by aggregating
many region-wise descriptors. In contrast to previous works employing
pre-trained deep networks as a black box to produce features, our method
leverages a deep architecture trained for the specific task of image retrieval.
Our contribution is twofold: (i) we leverage a ranking framework to learn
convolution and projection weights that are used to build the region features;
and (ii) we employ a region proposal network to learn which regions should be
pooled to form the final global descriptor. We show that using clean training
data is key to the success of our approach. To that aim, we use a large scale
but noisy landmark dataset and develop an automatic cleaning approach. The
proposed architecture produces a global image representation in a single
forward pass. Our approach significantly outperforms previous approaches based
on global descriptors on standard datasets. It even surpasses most prior works
based on costly local descriptor indexing and spatial verification. Additional
material is available at www.xrce.xerox.com/Deep-Image-Retrieval.

本文提出了一种新方法进行实例级别的图像检索，通过聚合多个基于区域的描述符为每个图像生成全局紧凑的固定长度表示，该方法基于一个特定图像检索任务训练的深度架构，利用排序框架来学习卷积和投影权重，并采用区域建议网络来学习应汇集哪些区域以形成最终的全局描述符。