Adaptive Poincaré Point to Set Distance for Few-Shot Classification (with Rongkai Ma, Pengfei Fang and Mehrtash Harandi)

This paper shows how to do few-shot classification by embedding both query points and class support sets into hyperbolic (Poincaré ball) space. The geometry is a natural fit because hyperbolic space expands exponentially with radius, giving more room for fine-grained distinctions between classes than Euclidean space. The proposed adaptive point-to-set distance learns a per-task notion of how far a query is from each support set, yielding consistent gains on standard few-shot benchmarks.

[AAAI 2022 paper]