Abstract
Domain generalization (DG) for object detection aims to enhance detectors' performance in unseen scenarios. This task remains challenging due to complex variations in real-world applications. Recently, diffusion models have demonstrated remarkable capabilities in diverse scene generation, which inspires us to explore their potential for improving DG tasks. Instead of generating images, our method extracts multi-step intermediate features during the diffusion process to obtain domain-invariant features for generalized detection. Furthermore, we propose an efficient knowledge transfer framework that enables detectors to inherit the generalization capabilities of diffusion models through feature and object-level alignment, without increasing inference time. We conduct extensive experiments on six challenging DG benchmarks. The results demonstrate that our method achieves substantial improvements of 14.0% mAP over existing DG approaches across different domains and corruption types. Notably, our method even outperforms most domain adaptation methods without accessing any target domain data. Moreover, the diffusion-guided detectors show consistent improvements of 15.9% mAP on average compared to the baseline. Our work aims to present an effective approach for domain-generalized detection and provide potential insights for robust visual recognition in real-world scenarios. The code is available at this https URL.
Abstract (translated)
领域泛化（Domain Generalization，DG）在目标检测中的应用旨在提高检测器在未见过场景下的表现。由于实际应用场景中存在复杂多变的情况，这一任务依然充满挑战。最近，扩散模型展示了其在生成多样化场景方面的卓越能力，这激发了我们将它们用于改善DG任务的潜力的研究兴趣。与直接生成图像不同，我们的方法通过在扩散过程中提取多步中间特征来获取跨域不变性特征，从而实现泛化检测。 此外，我们提出了一种高效的知识转移框架，使检测器能够继承扩散模型的泛化能力，而无需增加推理时间，并且该框架是通过特征级和对象级对齐完成的。我们在六个具有挑战性的DG基准数据集上进行了广泛的实验，结果显示我们的方法在不同域和损坏类型上比现有DG方法提高了14.0% mAP（平均精度）。值得注意的是，即使不访问目标域数据，我们提出的方法也超越了大多数领域适应方法的表现。 此外，由扩散模型指导的检测器相比基准线，在所有测试场景中平均mAP值提升达到了15.9%。我们的工作旨在提供一种有效的跨领域泛化检测解决方案，并为在实际场景中的稳健视觉识别提供潜在见解。代码可以在以下链接获取：[此链接](https://this https URL)。 请注意，最后一个URL应替换为您实际提供的代码仓库地址或其他相关资源的正确链接。
URL
https://arxiv.org/abs/2503.02101
