  Nearest-Neighbor (NN) classification has been proven as a simple and
effective approach for few-shot learning. The query data can be classified
efficiently by finding the nearest support class based on features extracted by
pretrained deep models. However, NN-based methods are sensitive to the data
distribution and may produce false prediction if the samples in the support set
happen to lie around the distribution boundary of different classes. To solve
this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot
classification method empowered by prior-driven data calibration. Inspired by
the distribution calibration technique which utilizes the distribution or
statistics of the base classes to calibrate the data for few-shot tasks, we
propose a novel discrete data calibration operation which is more suitable for
NN-based few-shot classification. Specifically, we treat the prototypes
representing each base class as priors and calibrate each support data based on
its similarity to different base prototypes. Then, we perform NN classification
using these discretely calibrated support data. Results from extensive
experiments on various datasets show our efficient non-learning based method
can outperform or at least comparable to SOTA methods which need additional
learning steps.
