  Current person image retrieval methods have achieved great improvements in
accuracy metrics. However, they rarely describe the reliability of the
prediction. In this paper, we propose an Uncertainty-Aware Learning (UAL)
method to remedy this issue. UAL aims at providing reliability-aware
predictions by considering data uncertainty and model uncertainty
simultaneously. Data uncertainty captures the ``noise" inherent in the sample,
while model uncertainty depicts the model's confidence in the sample's
prediction. Specifically, in UAL, (1) we propose a sampling-free data
uncertainty learning method to adaptively assign weights to different samples
during training, down-weighting the low-quality ambiguous samples. (2) we
leverage the Bayesian framework to model the model uncertainty by assuming the
parameters of the network follow a Bernoulli distribution. (3) the data
uncertainty and the model uncertainty are jointly learned in a unified network,
and they serve as two fundamental criteria for the reliability assessment: if a
probe is high-quality (low data uncertainty) and the model is confident in the
prediction of the probe (low model uncertainty), the final ranking will be
assessed as reliable. Experiments under the risk-controlled settings and the
multi-query settings show the proposed reliability assessment is effective. Our
method also shows superior performance on three challenging benchmarks under
the vanilla single query settings.
