The visual prompts have provided an efficient manner in addressing visual cross-domain problems. In previous works, Visual Domain Prompt (VDP) first introduces domain prompts to tackle the classification Test-Time Adaptation (TTA) problem by warping image-level prompts on the input and fine-tuning prompts for each target domain. However, since the image-level prompts mask out continuous spatial details in the prompt-allocated region, it will suffer from inaccurate contextual information and limited domain knowledge extraction, particularly when dealing with dense prediction TTA problems. To overcome these challenges, we propose a novel Sparse Visual Domain Prompts (SVDP) approach, which holds minimal trainable parameters (e.g., 0.1\%) in the image-level prompt and reserves more spatial information of the input. To better apply SVDP in extracting domain-specific knowledge, we introduce the Domain Prompt Placement (DPP) method to adaptively allocates trainable parameters of SVDP on the pixels with large distribution shifts. Furthermore, recognizing that each target domain sample exhibits a unique domain shift, we design Domain Prompt Updating (DPU) strategy to optimize prompt parameters differently for each sample, facilitating efficient adaptation to the target domain. Extensive experiments were conducted on widely-used TTA and continual TTA benchmarks, and our proposed method achieves state-of-the-art performance in both semantic segmentation and depth estimation tasks.

翻译：视觉提示为解决视觉跨域问题提供了一种高效方法。在先前工作中，视觉域提示（VDP）首次引入域提示，通过将图像级提示扭曲到输入上并为每个目标域微调提示，来处理分类测试时自适应（TTA）问题。然而，由于图像级提示会掩盖提示分配区域中的连续空间细节，当处理密集预测TTA问题时，它将面临不准确的上下文信息和有限的域知识提取问题。为克服这些挑战，我们提出了一种新颖的稀疏视觉域提示（SVDP）方法，该方法在图像级提示中仅包含少量可训练参数（例如0.1%），并保留了更多输入的空间信息。为更好地应用SVDP来提取域特定知识，我们引入了域提示放置（DPP）方法，自适应地将SVDP的可训练参数分配给具有较大分布偏移的像素。此外，鉴于每个目标域样本表现出独特的域偏移，我们设计了域提示更新（DPU）策略，为每个样本以不同方式优化提示参数，促进对目标域的高效自适应。在广泛使用的TTA和持续TTA基准上进行了大量实验，我们提出的方法在语义分割和深度估计任务中均达到了最先进的性能。