  Data uncertainty in practical person reID is ubiquitous, hence it requires
not only learning the discriminative features, but also modeling the
uncertainty based on the input. This paper proposes to learn the sample
posterior and the class prior distribution in the latent space, so that not
only representative features but also the uncertainty can be built by the
model. The prior reflects the distribution of all data in the same class, and
it is the trainable model parameters. While the posterior is the probability
density of a single sample, so it is actually the feature defined on the input.
We assume that both of them are in Gaussian form. To simultaneously model them,
we put forward a distribution loss, which measures the KL divergence from the
posterior to the priors in the manner of supervised learning. In addition, we
assume that the posterior variance, which is essentially the uncertainty, is
supposed to have the second-order characteristic. Therefore, a $\Sigma-$net is
proposed to compute it by the high order representation from its input.
Extensive experiments have been carried out on Market1501, DukeMTMC, MARS and
noisy dataset as well.
