  Few-shot object detection (FSOD) aims to achieve object detection only using
a few novel class training data. Most of the existing methods usually adopt a
transfer-learning strategy to construct the novel class distribution by
transferring the base class knowledge. However, this direct way easily results
in confusion between the novel class and other similar categories in the
decision space. To address the problem, we propose generating local reverse
samples (LRSamples) in Prototype Reference Frames to adaptively adjust the
center position and boundary range of the novel class distribution to learn
more discriminative novel class samples for FSOD. Firstly, we propose a Center
Calibration Variance Augmentation (CCVA) module, which contains the selection
rule of LRSamples, the generator of LRSamples, and augmentation on the
calibrated distribution centers. Specifically, we design an intra-class feature
converter (IFC) as the generator of CCVA to learn the selecting rule. By
transferring the knowledge of IFC from the base training to fine-tuning, the
IFC generates plentiful novel samples to calibrate the novel class
distribution. Moreover, we propose a Feature Density Boundary Optimization
(FDBO) module to adaptively adjust the importance of samples depending on their
distance from the decision boundary. It can emphasize the importance of the
high-density area of the similar class (closer decision boundary area) and
reduce the weight of the low-density area of the similar class (farther
decision boundary area), thus optimizing a clearer decision boundary for each
category. We conduct extensive experiments to demonstrate the effectiveness of
our proposed method. Our method achieves consistent improvement on the Pascal
VOC and MS COCO datasets based on DeFRCN and MFDC baselines.
