  We address the problem of cross-domain image retrieval, considering the
following practical application: given a user photo depicting a clothing image,
our goal is to retrieve the same or attribute-similar clothing items from
online shopping stores. This is a challenging problem due to the large
discrepancy between online shopping images, usually taken in ideal
lighting/pose/background conditions, and user photos captured in uncontrolled
conditions. To address this problem, we propose a Dual Attribute-aware Ranking
Network (DARN) for retrieval feature learning. More specifically, DARN consists
of two sub-networks, one for each domain, whose retrieval feature
representations are driven by semantic attribute learning. We show that this
attribute-guided learning is a key factor for retrieval accuracy improvement.
In addition, to further align with the nature of the retrieval problem, we
impose a triplet visual similarity constraint for learning to rank across the
two sub-networks. Another contribution of our work is a large-scale dataset
which makes the network learning feasible. We exploit customer review websites
to crawl a large set of online shopping images and corresponding offline user
photos with fine-grained clothing attributes, i.e., around 450,000 online
shopping images and about 90,000 exact offline counterpart images of those
online ones. All these images are collected from real-world consumer websites
reflecting the diversity of the data modality, which makes this dataset unique
and rare in the academic community. We extensively evaluate the retrieval
performance of networks in different configurations. The top-20 retrieval
accuracy is doubled when using the proposed DARN other than the current popular
solution using pre-trained CNN features only (0.570 vs. 0.268).
