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Nearest-Neighbor (NN) classification has been proven as a simple andeffective approach for few-shot learning. The query data can be classifiedefficiently by finding the nearest support class based on features extracted bypretrained deep models. However, NN-based methods are sensitive to the datadistribution and may produce false prediction if the samples in the support sethappen to lie around the distribution boundary of different classes. To solvethis issue, we present P3DC-Shot, an improved nearest-neighbor based few-shotclassification method empowered by prior-driven data calibration. Inspired bythe distribution calibration technique which utilizes the distribution orstatistics of the base classes to calibrate the data for few-shot tasks, wepropose a novel discrete data calibration operation which is more suitable forNN-based few-shot classification. Specifically, we treat the prototypesrepresenting each base class as priors and calibrate each support data based onits similarity to different base prototypes. Then, we perform NN classificationusing these discretely calibrated support data. Results from extensiveexperiments on various datasets show our efficient non-learning based methodcan outperform or at least comparable to SOTA methods which need additionallearning steps.

P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classification