Object detection methods under known single degradations have been extensively investigated. However, existing approaches require prior knowledge of the degradation type and train a separate model for each, limiting their practical applications in unpredictable environments. To address this challenge, we propose a chain-of-thought (CoT) prompted adaptive enhancer, CPA-Enhancer, for object detection under unknown degradations. Specifically, CPA-Enhancer progressively adapts its enhancement strategy under the step-by-step guidance of CoT prompts, that encode degradation-related information. To the best of our knowledge, it's the first work that exploits CoT prompting for object detection tasks. Overall, CPA-Enhancer is a plug-and-play enhancement model that can be integrated into any generic detectors to achieve substantial gains on degraded images, without knowing the degradation type priorly. Experimental results demonstrate that CPA-Enhancer not only sets the new state of the art for object detection but also boosts the performance of other downstream vision tasks under unknown degradations.

翻译：针对已知单一退化的目标检测方法已被广泛研究。然而，现有方法需预先获知退化类型并为每种退化训练单独模型，这限制了其在不可预测环境中的实际应用。为应对这一挑战，我们提出了一种基于链式思维（CoT）提示的自适应增强器——CPA-Enhancer，用于未知退化条件下的目标检测。具体而言，CPA-Enhancer在编码退化相关信息的CoT提示逐步引导下，自适应调整其增强策略。据我们所知，这是首个将CoT提示应用于目标检测任务的研究。总体而言，CPA-Enhancer是一种即插即用的增强模型，无需预先知晓退化类型即可集成至任意通用检测器，在退化图像上实现显著性能提升。实验结果表明，CPA-Enhancer不仅在目标检测任务上确立了新的最佳性能，还提升了其他下游视觉任务在未知退化条件下的表现。