跨越迁移：通过动态风格桥接实现向前促进的持续测试时间适应
Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging
AI总结：
本文提出了一种新的向前促进的持续测试时间适应方法，通过动态风格桥接机制，在部署前构建紧凑的知识库，并在测试时动态注入输入数据风格，以提供可靠的监督信号，从而在持续迁移中实现稳定的适应。
摘要：
持续测试时间适应（CTTA）旨在使感知系统能够处理部署后遇到的动态分布偏移。现有方法主要采用后向对齐范式，这种范式将输入数据与源域衍生的监督代理进行刚性对齐，因此在面对不可靠的监督和不断变化的分布偏移时表现不佳。为克服这些限制，我们引入了一种新的向前促进范式，通过一种称为动态风格桥接的方法。在部署前，我们构建了一个生成类示例的紧凑知识库。在测试时间，为了减轻固有的生成偏移并使这些代理适应输入数据，我们提出了一个多级桥接机制。该机制在输入、统计和表示层动态地将代理与输入数据风格注入，同时保留代理的原始语义。这些高保真的代理随后被用来提供可靠且按需的监督信号，从而在持续偏移下实现稳定的适应。在标准CTTA基准上的广泛实验表明，我们的方法在最近的最先进方法上实现了一致且显著的改进。代码可在https://github.com/z1358/DAS上获得。
英文摘要：
Continual Test-Time Adaptation (CTTA) aims to empower perception systems to handle dynamic distribution shifts encountered after deployment. Existing methods predominantly follow a backward-alignment paradigm, which rigidly aligns incoming data with supervisory surrogates derived from the source domain. Consequently, they struggle with unreliable supervision and evolving distribution shifts. To overcome these limitations, we introduce a novel forward-facilitation paradigm through a method termed Dynamic Style Bridging. Prior to deployment, we construct a compact knowledge base of generated class exemplars. During test time, to mitigate inherent generative bias and adapt these proxies to incoming data, we propose a multi-level bridging mechanism. This mechanism dynamically injects the proxies with incoming data styles at the input, statistical, and representation levels, while preserving the original semantics of the proxies. These high-fidelity proxies are then used to provide reliable, on-demand supervisory signals, enabling stable adaptation under continual shifts. Extensive experiments across standard CTTA benchmarks demonstrate that our method achieves consistent and substantial improvements over recent state-of-the-art approaches. Code is available at \href{https://github.com/z1358/DAS}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
