  The one-shot Person Re-ID scenario faces two kinds of uncertainties when
constructing the prediction model from $X$ to $Y$. The first is model
uncertainty, which captures the noise of the parameters in DNNs due to a lack
of training data. The second is data uncertainty, which can be divided into two
sub-types: one is image noise, where severe occlusion and the complex
background contain irrelevant information about the identity; the other is
label noise, where mislabeled affects visual appearance learning. In this
paper, to tackle these issues, we propose a novel Self-Paced Uncertainty
Estimation Network (SPUE-Net) for one-shot Person Re-ID. By introducing a
self-paced sampling strategy, our method can estimate the pseudo-labels of
unlabeled samples iteratively to expand the labeled samples gradually and
remove model uncertainty without extra supervision. We divide the pseudo-label
samples into two subsets to make the use of training samples more reasonable
and effective. In addition, we apply a Co-operative learning method of local
uncertainty estimation combined with determinacy estimation to achieve better
hidden space feature mining and to improve the precision of selected
pseudo-labeled samples, which reduces data uncertainty. Extensive comparative
evaluation experiments on video-based and image-based datasets show that
SPUE-Net has significant advantages over the state-of-the-art methods.
