  Adapting object detectors learned with sufficient supervision to novel
classes under low data regimes is charming yet challenging. In few-shot object
detection (FSOD), the two-step training paradigm is widely adopted to mitigate
the severe sample imbalance, i.e., holistic pre-training on base classes, then
partial fine-tuning in a balanced setting with all classes. Since unlabeled
instances are suppressed as backgrounds in the base training phase, the learned
RPN is prone to produce biased proposals for novel instances, resulting in
dramatic performance degradation. Unfortunately, the extreme data scarcity
aggravates the proposal distribution bias, hindering the RoI head from evolving
toward novel classes. In this paper, we introduce a simple yet effective
proposal distribution calibration (PDC) approach to neatly enhance the
localization and classification abilities of the RoI head by recycling its
localization ability endowed in base training and enriching high-quality
positive samples for semantic fine-tuning. Specifically, we sample proposals
based on the base proposal statistics to calibrate the distribution bias and
impose additional localization and classification losses upon the sampled
proposals for fast expanding the base detector to novel classes. Experiments on
the commonly used Pascal VOC and MS COCO datasets with explicit
state-of-the-art performances justify the efficacy of our PDC for FSOD. Code is
available at github.com/Bohao-Lee/PDC.
