  The goal of Universal Cross-Domain Retrieval (UCDR) is to achieve robust
performance in generalized test scenarios, wherein data may belong to strictly
unknown domains and categories during training. Recently, pre-trained models
with prompt tuning have shown strong generalization capabilities and attained
noteworthy achievements in various downstream tasks, such as few-shot learning
and video-text retrieval. However, applying them directly to UCDR may not
sufficiently to handle both domain shift (i.e., adapting to unfamiliar domains)
and semantic shift (i.e., transferring to unknown categories). To this end, we
propose \textbf{Pro}mpting-to-\textbf{S}imulate (ProS), the first method to
apply prompt tuning for UCDR. ProS employs a two-step process to simulate
Content-aware Dynamic Prompts (CaDP) which can impact models to produce
generalized features for UCDR. Concretely, in Prompt Units Learning stage, we
introduce two Prompt Units to individually capture domain and semantic
knowledge in a mask-and-align way. Then, in Context-aware Simulator Learning
stage, we train a Content-aware Prompt Simulator under a simulated test
scenarios to produce the corresponding CaDP. Extensive experiments conducted on
three benchmark datasets show that our method achieves new state-of-the-art
performance without bringing excessive parameters. Our method is publicly
available at https://github.com/fangkaipeng/ProS.
