LEAP:D —— 一种用于领域泛化航空目标检测的基于提示词的新颖方法
LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection
AI总结：
针对无人机图像多变条件导致目标检测性能下降的问题，提出基于可学习提示词的视觉-语言方法LEAP:D，通过一步法同步更新提示词与模型训练，有效减少领域干扰并提升跨环境航空目标检测的鲁棒性。
摘要：
无人机拍摄的图像因拍摄条件多变而给目标检测带来重大挑战，这些条件会改变目标的表观和形状。无人机高度、角度和天气等因素导致了这些变化，进而影响目标检测算法的性能。为应对这些挑战，我们引入了一种使用可学习提示词的创新视觉-语言方法。这种从传统手动提示词的转变旨在减少特定领域知识的干扰，最终提升目标检测能力。此外，我们采用一步法简化训练过程，在模型训练的同时更新可学习提示词，在不影响性能的前提下提高了效率。我们的研究通过利用可学习提示词和优化训练过程，为领域泛化目标检测做出了贡献。这增强了模型在多样环境中的鲁棒性和适应性，从而实现更有效的航空目标检测。
英文摘要：
Drone-captured images present significant challenges in object detection due to varying shooting conditions, which can alter object appearance and shape. Factors such as drone altitude, angle, and weather cause these variations, influencing the performance of object detection algorithms. To tackle these challenges, we introduce an innovative vision-language approach using learnable prompts. This shift from conventional manual prompts aims to reduce domain-specific knowledge interference, ultimately improving object detection capabilities. Furthermore, we streamline the training process with a one-step approach, updating the learnable prompt concurrently with model training, enhancing efficiency without compromising performance. Our study contributes to domain-generalized object detection by leveraging learnable prompts and optimizing training processes. This enhances model robustness and adaptability across diverse environments, leading to more effective aerial object detection.
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。
