  Domain generalization in person re-identification is a highly important
meaningful and practical task in which a model trained with data from several
source domains is expected to generalize well to unseen target domains. Domain
adversarial learning is a promising domain generalization method that aims to
remove domain information in the latent representation through adversarial
training. However, in person re-identification, the domain and class are
correlated, and we theoretically show that domain adversarial learning will
lose certain information about class due to this domain-class correlation.
Inspired by casual inference, we propose to perform interventions to the domain
factor $d$, aiming to decompose the domain-class correlation. To achieve this
goal, we proposed estimating the resulting representation $z^{*}$ caused by the
intervention through first- and second-order statistical characteristic
matching. Specifically, we build a memory bank to restore the statistical
characteristics of each domain. Then, we use the newly generated samples
$\{z^{*},y,d^{*}\}$ to compute the loss function. These samples are
domain-class correlation decomposed; thus, we can learn a domain-invariant
representation that can capture more class-related features. Extensive
experiments show that our model outperforms the state-of-the-art methods on the
large-scale domain generalization Re-ID benchmark.
