  As a challenging task, unsupervised person ReID aims to match the same
identity with query images which does not require any labeled information. In
general, most existing approaches focus on the visual cues only, leaving
potentially valuable auxiliary metadata information (e.g., spatio-temporal
context) unexplored. In the real world, such metadata is normally available
alongside captured images, and thus plays an important role in separating
several hard ReID matches. With this motivation in mind, we
propose~\textbf{MGH}, a novel unsupervised person ReID approach that uses meta
information to construct a hypergraph for feature learning and label
refinement. In principle, the hypergraph is composed of camera-topology-aware
hyperedges, which can model the heterogeneous data correlations across cameras.
Taking advantage of label propagation on the hypergraph, the proposed approach
is able to effectively refine the ReID results, such as correcting the wrong
labels or smoothing the noisy labels. Given the refined results, We further
present a memory-based listwise loss to directly optimize the average precision
in an approximate manner. Extensive experiments on three benchmarks demonstrate
the effectiveness of the proposed approach against the state-of-the-art.
