Title: Prompt-Matched Semantic Segmentation. Abstract: The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual foundation models to downstream tasks, e.g., image semantic segmentation. Conventional methods usually fine-tuned the entire networks for each specific dataset, which will be burdensome to store massive parameters of these networks. Several recent works attempted to insert some extra trainable parameters into the frozen networks to learn visual prompts for parameter-efficient tuning. However, these works showed poor generality as they were designed specifically for Transformers. Moreover, using limited information in these schemes, they exhibited a poor capacity to learn effective prompts. To alleviate these issues, we propose a novel Inter-Stage Prompt-Matched Framework for generic and effective visual prompt tuning. Specifically, to ensure generality, we divide the pre-trained backbone with frozen parameters into multiple stages and perform prompt learning between different stages, which makes the proposed scheme applicable to various architectures of CNN and Transformer. For effective tuning, a lightweight Semantic-aware Prompt Matcher (SPM) is designed to progressively learn reasonable prompts with a recurrent mechanism, guided by the rich information of interim semantic maps. Working as a deep matched filter of representation learning, the proposed SPM can well transform the output of the previous stage into a desirable input for the next stage, thus achieving the better matching/stimulating for the pre-trained knowledge. Finally, we apply the proposed method to handle various semantic segmentation tasks. Extensive experiments on five benchmarks show that the proposed scheme can achieve a promising trade-off between parameter efficiency and performance effectiveness.