Dual Attention Matching Network for Context-Aware Feature Sequence based
Person Re-Identification
Abstract
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 endto-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 intrasequence 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 decorrelation 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