CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations

TL;DR

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.

Abstract

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.

Figures (16)

• Figure 1: Overview of the proposed CPA-Enhancer.
• Figure 2: We show t-SNE plots of the degradation embeddings in different decoder layers. It is worth noting that the clusters from Layer 3 to Layer 1 become more and more discriminant, indicating that our network has the ability to distinguish between different types of degradations under the step-by-step guidance of CoT prompts.
• Figure 3: Our proposed content-driven prompt block.
• Figure 4: Visual comparisons of CPA-Enhancer under the all-in-one setting.
• Figure 5: Visual comparison of CPA-Enhancer under the one-by-one setting.