Rethinking Generalization in Few-Shot Classification (with Markus Hiller, Rongkai Ma and Mehrtash Harandi)

This paper shows that the standard few-shot classification pipeline systematically underfits the rich structure of images. By framing few-shot classification in terms of patches rather than whole images and rethinking the sampling strategy over patches, the method obtains better-generalising features that close the gap to upper-bound oracle performance on standard miniImageNet and tieredImageNet benchmarks.

[NeurIPS 2022 paper]