  The vision-language pre-training has enabled deep models to make a huge step
forward in generalizing across unseen domains. The recent learning method based
on the vision-language pre-training model is a great tool for domain
generalization and can solve this problem to a large extent. However, there are
still some issues that an advancement still suffers from trading-off between
domain invariance and class separability, which are crucial in current DG
problems. However, there are still some issues that an advancement still
suffers from trading-off between domain invariance and class separability,
which are crucial in current DG problems. In this paper, we introduce a novel
prompt learning strategy that leverages deep vision prompts to address domain
invariance while utilizing language prompts to ensure class separability,
coupled with adaptive weighting mechanisms to balance domain invariance and
class separability. Extensive experiments demonstrate that deep vision prompts
effectively extract domain-invariant features, significantly improving the
generalization ability of deep models and achieving state-of-the-art
performance on three datasets.
