  Under the domain shift, cross-domain few-shot object detection aims to adapt
object detectors in the target domain with a few annotated target data. There
exists two significant challenges: (1) Highly insufficient target domain data;
(2) Potential over-adaptation and misleading caused by inappropriately
amplified target samples without any restriction. To address these challenges,
we propose an adaptive method consisting of two parts. First, we propose an
adaptive optimization strategy to select augmented data similar to target
samples rather than blindly increasing the amount. Specifically, we filter the
augmented candidates which significantly deviate from the target feature
distribution in the very beginning. Second, to further relieve the data
limitation, we propose the multi-level domain-aware data augmentation to
increase the diversity and rationality of augmented data, which exploits the
cross-image foreground-background mixture. Experiments show that the proposed
method achieves state-of-the-art performance on multiple benchmarks.
