  Domain generalization (DG) aims to learn a model that generalizes well to
unseen target domains utilizing multiple source domains without re-training.
Most existing DG works are based on convolutional neural networks (CNNs).
However, the local operation of the convolution kernel makes the model focus
too much on local representations (e.g., texture), which inherently causes the
model more prone to overfit to the source domains and hampers its
generalization ability. Recently, several MLP-based methods have achieved
promising results in supervised learning tasks by learning global interactions
among different patches of the image. Inspired by this, in this paper, we first
analyze the difference between CNN and MLP methods in DG and find that MLP
methods exhibit a better generalization ability because they can better capture
the global representations (e.g., structure) than CNN methods. Then, based on a
recent lightweight MLP method, we obtain a strong baseline that outperforms
most state-of-the-art CNN-based methods. The baseline can learn global
structure representations with a filter to suppress structure irrelevant
information in the frequency space. Moreover, we propose a dynAmic
LOw-Frequency spectrum Transform (ALOFT) that can perturb local texture
features while preserving global structure features, thus enabling the filter
to remove structure-irrelevant information sufficiently. Extensive experiments
on four benchmarks have demonstrated that our method can achieve great
performance improvement with a small number of parameters compared to SOTA
CNN-based DG methods. Our code is available at
https://github.com/lingeringlight/ALOFT/.
