  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.
