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.

本文提出一种新方法以应对跨域少样本目标检测面临的数据不足及误导等问题，该方法由自适应优化策略与多层次领域感知数据增强两部分构成，最终在多个基准测试中取得最优表现。