  Most existing re-identification methods focus on learning robust and
discriminative features with deep convolution networks. However, many of them
consider content similarity separately and fail to utilize the context
information of the query and gallery sets, e.g. probe-gallery and
gallery-gallery relations, thus hard samples may not be well solved due to the
limited or even misleading information. In this paper, we present a novel
Context-Aware Graph Convolution Network (CAGCN), where the probe-gallery
relations are encoded into the graph nodes and the graph edge connections are
well controlled by the gallery-gallery relations. In this way, hard samples can
be addressed with the context information flows among other easy samples during
the graph reasoning. Specifically, we adopt an effective hard gallery sampler
to obtain high recall for positive samples while keeping a reasonable graph
size, which can also weaken the imbalanced problem in training process with low
computation complexity.Experiments show that the proposed method achieves
state-of-the-art performance on both person and vehicle re-identification
datasets in a plug and play fashion with limited overhead.
