  Our impression about one person often updates after we see more aspects of
him/her and this process keeps iterating given more meetings. We formulate such
an intuition into the problem of person re-identification (re-ID), where the
representation of a query (probe) image is iteratively updated with new
information from the candidates in the gallery. Specifically, we propose a
simple attentional aggregation formulation to instantiate this idea and
showcase that such a pipeline achieves competitive performance on standard
benchmarks including CUHK03, Market-1501 and DukeMTMC. Not only does such a
simple method improve the performance of the baseline models, it also achieves
comparable performance with latest advanced re-ranking methods. Another
advantage of this proposal is its flexibility to incorporate different
representations and similarity metrics. By utilizing stronger representations
and metrics, we further demonstrate state-of-the-art person re-ID performance,
which also validates the general applicability of the proposed method.
