  Domain generalization (DG) for person re-identification (ReID) is a
challenging problem, as access to target domain data is not permitted during
the training process. Most existing DG ReID methods update the feature
extractor and classifier parameters based on the same features. This common
practice causes the model to overfit to existing feature styles in the source
domain, resulting in sub-optimal generalization ability on target domains. To
solve this problem, we propose a novel style interleaved learning (IL)
framework. Unlike conventional learning strategies, IL incorporates two forward
propagations and one backward propagation for each iteration. We employ the
features of interleaved styles to update the feature extractor and classifiers
using different forward propagations, which helps to prevent the model from
overfitting to certain domain styles. To generate interleaved feature styles,
we further propose a new feature stylization approach. It produces a wide range
of meaningful styles that are both different and independent from the original
styles in the source domain, which caters to the IL methodology. Extensive
experimental results show that our model not only consistently outperforms
state-of-the-art methods on large-scale benchmarks for DG ReID, but also has
clear advantages in computational efficiency. The code is available at
https://github.com/WentaoTan/Interleaved-Learning.
