  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.
