  Cross-domain visual data matching is one of the fundamental problems in many
real-world vision tasks, e.g., matching persons across ID photos and
surveillance videos. Conventional approaches to this problem usually involves
two steps: i) projecting samples from different domains into a common space,
and ii) computing (dis-)similarity in this space based on a certain distance.
In this paper, we present a novel pairwise similarity measure that advances
existing models by i) expanding traditional linear projections into affine
transformations and ii) fusing affine Mahalanobis distance and Cosine
similarity by a data-driven combination. Moreover, we unify our similarity
measure with feature representation learning via deep convolutional neural
networks. Specifically, we incorporate the similarity measure matrix into the
deep architecture, enabling an end-to-end way of model optimization. We
extensively evaluate our generalized similarity model in several challenging
cross-domain matching tasks: person re-identification under different views and
face verification over different modalities (i.e., faces from still images and
videos, older and younger faces, and sketch and photo portraits). The
experimental results demonstrate superior performance of our model over other
state-of-the-art methods.
