CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations
<|reference_start|>CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations: 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.<|reference_end|>
@article{zhang2024cpa-enhancer:,
    title={CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object
  Detection under Unknown Degradations},
    author={Yuwei Zhang, Yan Wu, Yanming Liu, Xinyue Peng},
    journal={arXiv preprint arXiv:2403.11220},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11220},
    primaryClass={cs.CV cs.AI cs.LG}
}