  Typical person re-identification (ReID) methods usually describe each
pedestrian with a single feature vector and match them in a task-specific
metric space. However, the methods based on a single feature vector are not
sufficient enough to overcome visual ambiguity, which frequently occurs in real
scenario. In this paper, we propose a novel end-to-end trainable framework,
called Dual ATtention Matching network (DuATM), to learn context-aware feature
sequences and perform attentive sequence comparison simultaneously. The core
component of our DuATM framework is a dual attention mechanism, in which both
intra-sequence and inter-sequence attention strategies are used for feature
refinement and feature-pair alignment, respectively. Thus, detailed visual cues
contained in the intermediate feature sequences can be automatically exploited
and properly compared. We train the proposed DuATM network as a siamese network
via a triplet loss assisted with a de-correlation loss and a cross-entropy
loss. We conduct extensive experiments on both image and video based ReID
benchmark datasets. Experimental results demonstrate the significant advantages
of our approach compared to the state-of-the-art methods.
