With the proliferation of e-commerce websites and the ubiquitousness of smart
phones, cross-domain image retrieval using images taken by smart phones as
queries to search products on e-commerce websites is emerging as a popular
application. One challenge of this task is to locate the attention of both the
query and database images. In particular, database images, e.g. of fashion
products, on e-commerce websites are typically displayed with other
accessories, and the images taken by users contain noisy background and large
variations in orientation and lighting. Consequently, their attention is
difficult to locate. In this paper, we exploit the rich tag information
available on the e-commerce websites to locate the attention of database
images. For query images, we use each candidate image in the database as the
context to locate the query attention. Novel deep convolutional neural network
architectures, namely TagYNet and CtxYNet, are proposed to learn the attention
weights and then extract effective representations of the images. Experimental
results on public datasets confirm that our approaches have significant
improvement over the existing methods in terms of the retrieval accuracy and
efficiency.

本文旨在通过在电商网站上的标签信息和候选图像上下文信息，利用了 TagYNet 和 CtxYNet 等深度卷积神经网络架构，以实现对基于智能手机拍摄的图像进行检索的 Attention weights 计算和图像特征提取，可显著提高检索的准确率和效率。