To mitigate the detection performance drop caused by domain shift, we aim to
develop a novel few-shot adaptation approach that requires only a few target
domain images with limited bounding box annotations. To this end, we first
observe several significant challenges. First, the target domain data is highly
insufficient, making most existing domain adaptation methods ineffective.
Second, object detection involves simultaneous localization and classification,
further complicating the model adaptation process. Third, the model suffers
from over-adaptation (similar to overfitting when training with a few data
example) and instability risk that may lead to degraded detection performance
in the target domain. To address these challenges, we first introduce a pairing
mechanism over source and target features to alleviate the issue of
insufficient target domain samples. We then propose a bi-level module to adapt
the source trained detector to the target domain: 1) the split pooling based
image level adaptation module uniformly extracts and aligns paired local patch
features over locations, with different scale and aspect ratio; 2) the instance
level adaptation module semantically aligns paired object features while avoids
inter-class confusion. Meanwhile, a source model feature regularization (SMFR)
is applied to stabilize the adaptation process of the two modules. Combining
these contributions gives a novel few-shot adaptive Faster-RCNN framework,
termed FAFRCNN, which effectively adapts to target domain with a few labeled
samples. Experiments with multiple datasets show that our model achieves new
state-of-the-art performance under both the interested few-shot domain
adaptation(FDA) and unsupervised domain adaptation(UDA) setting.

提出一种名为 FAFRCNN 的基于源域数据训练的检测器的 few-shot adaptation 方法，通过配对机制和双层模块来实现对目标域中目标检测的准确性提高。