  Large pre-trained vision language models (VLMs) have shown impressive
zero-shot ability on downstream tasks with manually designed prompt. To further
adapt VLMs to downstream tasks, soft prompt is proposed to replace manually
designed prompt, which undergoes fine-tuning based on specific domain data.
Prior prompt learning methods primarily learn a fixed prompt or residuled
prompt from training samples. However, the learned prompts lack diversity and
ignore information about unseen domains. In this paper, we reframe the prompt
learning framework from a generative perspective and propose a simple yet
efficient method for the Domain Generalization (DG) task, namely Soft Prompt
Generation (SPG). Specifically, SPG consists of a two-stage training phase and
an inference phase. During the training phase, we introduce soft prompt label
for each domain, aiming to incorporate the generative model domain knowledge.
During the inference phase, the generator of the generative model is employed
to obtain instance-specific soft prompts for the unseen target domain.
Extensive experiments on five domain generalization benchmarks of three DG
tasks demonstrate that SPG achieves state-of-the-art performance. The code is
available at https://github.com/renytek13/Soft-Prompt-Generation-with-CGAN.
