  Video-based person re-identification (reID) aims at matching the same person
across video clips. It is a challenging task due to the existence of redundancy
among frames, newly revealed appearance, occlusion, and motion blurs. In this
paper, we propose an attentive feature aggregation module, namely
Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to
delicately aggregate spatio-temporal features into a discriminative video-level
feature representation. In order to determine the contribution/importance of a
spatial-temporal feature node, we propose to learn the attention from a global
view with convolutional operations. Specifically, we stack its relations, i.e.,
pairwise correlations with respect to a representative set of reference feature
nodes (S-RFNs) that represents global video information, together with the
feature itself to infer the attention. Moreover, to exploit the semantics of
different levels, we propose to learn multi-granularity attentions based on the
relations captured at different granularities. Extensive ablation studies
demonstrate the effectiveness of our attentive feature aggregation module
MG-RAFA. Our framework achieves the state-of-the-art performance on three
benchmark datasets.
