Adaptive Poincar\'e Point to Set Distance for Few-Shot Classification

TL;DR

This paper introduces a novel adaptive hyperbolic point-to-set distance metric for few-shot classification, improving robustness and accuracy by making the metric context-aware and task-dependent, with state-of-the-art results on multiple benchmarks.

Contribution

It proposes a new context-aware hyperbolic metric for point-to-set distance that adapts based on sample constellation, enhancing few-shot learning performance.

Findings

Achieves state-of-the-art results on five few-shot benchmarks.

Demonstrates robustness to outliers in few-shot classification.

Improves accuracy over baseline models with the proposed metric.

Abstract

Learning and generalizing from limited examples, i,e, few-shot learning, is of core importance to many real-world vision applications. A principal way of achieving few-shot learning is to realize an embedding where samples from different classes are distinctive. Recent studies suggest that embedding via hyperbolic geometry enjoys low distortion for hierarchical and structured data, making it suitable for few-shot learning. In this paper, we propose to learn a context-aware hyperbolic metric to characterize the distance between a point and a set associated with a learned set to set distance. To this end, we formulate the metric as a weighted sum on the tangent bundle of the hyperbolic space and develop a mechanism to obtain the weights adaptively and based on the constellation of the points. This not only makes the metric local but also dependent on the task in hand, meaning that the metric will adapt depending on the samples that it compares. We empirically show that such metric yields robustness in the presence of outliers and achieves a tangible improvement over baseline models. This includes the state-of-the-art results on five popular few-shot classification benchmarks, namely mini-ImageNet, tiered-ImageNet, Caltech-UCSD Birds-200-2011 (CUB), CIFAR-FS, and FC100.