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}.

本文提出了一种基于深度共同关注的比较器（DCCs），以克服现有人物重新识别方法在相互独立的图像区域上检测最相关的部分以及在比较相似性时的空间操作等限制，用于在行人图像对之间寻找相关特征并动态生成相对表示，提高了行人重新识别的效率和准确性。实验结果表明，该方法在三个基准数据集上均取得了最先进的性能。