  In this paper, we propose a study of the cross-domain few-shot object
detection (CD-FSOD) benchmark, consisting of image data from a diverse data
domain. On the proposed benchmark, we evaluate state-of-art FSOD approaches,
including meta-learning FSOD approaches and fine-tuning FSOD approaches. The
results show that these methods tend to fall, and even underperform the naive
fine-tuning model. We analyze the reasons for their failure and introduce a
strong baseline that uses a mutually-beneficial manner to alleviate the
overfitting problem. Our approach is remarkably superior to existing approaches
by significant margins (2.0\% on average) on the proposed benchmark. Our code
is available at \url{https://github.com/FSOD/CD-FSOD}.
