  Person re-identification (re-ID) requires rapid, flexible yet discriminant
representations to quickly generalize to unseen observations on-the-fly and
recognize the same identity across disjoint camera views. Recent effective
methods are developed in a pair-wise similarity learning system to detect a
fixed set of features from distinct regions which are mapped to their vector
embeddings for the distance measuring. However, the most relevant and crucial
parts of each image are detected independently without referring to the
dependency conditioned on one and another. Also, these region based methods
rely on spatial manipulation to position the local features in comparable
similarity measuring. To combat these limitations, in this paper we introduce
the Deep Co-attention based Comparators (DCCs) that fuse the co-dependent
representations of the paired images so as to focus on the relevant parts of
both images and produce their \textit{relative representations}. Given a pair
of pedestrian images to be compared, the proposed model mimics the foveation of
human eyes to detect distinct regions concurrent on both images, namely
co-dependent features, and alternatively attend to relevant regions to fuse
them into the similarity learning. Our comparator is capable of producing
dynamic representations relative to a particular sample every time, and thus
well-suited to the case of re-identifying pedestrians on-the-fly. We perform
extensive experiments to provide the insights and demonstrate the effectiveness
of the proposed DCCs in person re-ID. Moreover, our approach has achieved the
state-of-the-art performance on three benchmark data sets: DukeMTMC-reID
\cite{DukeMTMC}, CUHK03 \cite{FPNN}, and Market-1501 \cite{Market1501}.
