Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain. Prior works typically require the access to the source domain data for adaptation, and the availability of sufficient data on the target domain. However, these assumptions may not hold due to data privacy and rare data collection. In this paper, we propose and investigate a more practical and challenging domain adaptive object detection problem under both source-free and few-shot conditions, named as SF-FSDA. To overcome this problem, we develop an efficient labeled data factory based approach. Without accessing the source domain, the data factory renders i) infinite amount of synthesized target-domain like images, under the guidance of the few-shot image samples and text description from the target domain; ii) corresponding bounding box and category annotations, only demanding minimum human effort, i.e., a few manually labeled examples. On the one hand, the synthesized images mitigate the knowledge insufficiency brought by the few-shot condition. On the other hand, compared to the popular pseudo-label technique, the generated annotations from data factory not only get rid of the reliance on the source pretrained object detection model, but also alleviate the unavoidably pseudo-label noise due to domain shift and source-free condition. The generated dataset is further utilized to adapt the source pretrained object detection model, realizing the robust object detection under SF-FSDA. The experiments on different settings showcase that our proposed approach outperforms other state-of-the-art methods on SF-FSDA problem. Our codes and models will be made publicly available.

翻译：域自适应目标检测旨在利用从有标注源域学到的知识，提升在无标注目标域上的性能。现有方法通常需要访问源域数据进行适配，且需要目标域数据量充足。然而，由于数据隐私和稀有数据采集等问题，这些假设往往难以成立。本文提出并研究了一种更具实用性和挑战性的域自适应目标检测问题——在无源且少样本条件下的SF-FSDA。为解决该问题，我们开发了一种基于高效标注数据工厂的方法。在不访问源域的情况下，数据工厂在目标域少量样本图像和文本描述的引导下，生成：i）无限数量的合成目标域类图像；ii）对应的边界框和类别标注，仅需最少人力投入，即少量人工标注示例。一方面，合成图像缓解了少样本条件带来的知识不足问题。另一方面，与流行的伪标签技术相比，数据工厂生成的标注不仅摆脱了对源域预训练目标检测模型的依赖，还避免了因域偏移和无源条件而产生的不可避免的伪标签噪声。进一步利用生成的数据集适配源域预训练目标检测模型，实现了SF-FSDA下的鲁棒目标检测。在不同设置下的实验表明，所提方法在SF-FSDA问题上优于其他最先进方法。我们的代码和模型将公开提供。