  Few-shot object detection(FSOD) aims to design methods to adapt object
detectors efficiently with only few annotated samples. Fine-tuning has been
shown to be an effective and practical approach. However, previous works often
take the classical base-novel two stage fine-tuning procedure but ignore the
implicit stability-plasticity contradiction among different modules.
Specifically, the random re-initialized classifiers need more plasticity to
adapt to novel samples. The other modules inheriting pre-trained weights demand
more stability to reserve their class-agnostic knowledge. Regular fine-tuning
which couples the optimization of these two parts hurts the model
generalization in FSOD scenarios. In this paper, we find that this problem is
prominent in the end-to-end object detector Sparse R-CNN for its
multi-classifier cascaded architecture. We propose to mitigate this
contradiction by a new three-stage fine-tuning procedure by introducing an
addtional plasticity classifier fine-tuning(PCF) stage. We further design the
multi-source ensemble(ME) technique to enhance the generalization of the model
in the final fine-tuning stage. Extensive experiments verify that our method is
effective in regularizing Sparse R-CNN, outperforming previous methods in the
FSOD benchmark.
