  Recently, few-shot object detection~(FSOD) has received much attention from
the community, and many methods are proposed to address this problem from a
knowledge transfer perspective. Though promising results have been achieved,
these methods fail to achieve shot-stable:~methods that excel in low-shot
regimes are likely to struggle in high-shot regimes, and vice versa. We believe
this is because the primary challenge of FSOD changes when the number of shots
varies. In the low-shot regime, the primary challenge is the lack of
inner-class variation. In the high-shot regime, as the variance approaches the
real one, the main hindrance to the performance comes from misalignment between
learned and true distributions. However, these two distinct issues remain
unsolved in most existing FSOD methods. In this paper, we propose to overcome
these challenges by exploiting rich knowledge the model has learned and
effectively transferring them to the novel classes. For the low-shot regime, we
propose a distribution calibration method to deal with the lack of inner-class
variation problem. Meanwhile, a shift compensation method is proposed to
compensate for possible distribution shift during fine-tuning. For the
high-shot regime, we propose to use the knowledge learned from ImageNet as
guidance for the feature learning in the fine-tuning stage, which will
implicitly align the distributions of the novel classes. Although targeted
toward different regimes, these two strategies can work together to further
improve the FSOD performance. Experiments on both the VOC and COCO benchmarks
show that our proposed method can significantly outperform the baseline method
and produce competitive results in both low-shot settings (shot<5) and
high-shot settings (shot>=5). Code is available at
https://github.com/JulioZhao97/EffTrans_Fsdet.git.
