Improved Few-Shot Visual Classification
Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data. Most few-shot learning approaches to date have focused on progressively more complex neural feature extractors and classifier adaptation strategies, as well as the refinement of the task definition itself. In this paper, we explore the hypothesis that a simple class-covariance-based distance metric, namely the Mahalanobis distance, adopted into a state of the art few-shot learning approach (CNAPS) can, in and of itself, lead to a significant performance improvement. We also discover that it is possible to learn adaptive feature extractors that allow useful estimation of the high dimensional feature covariances required by this metric from surprisingly few samples. The result of our work is a new "Simple CNAPS" architecture which has up to 9.2% fewer trainable parameters than CNAPS and performs up to 6.1% better than state of the art on the standard few-shot image classification benchmark dataset.
PDF Abstract CVPR 2020 PDF CVPR 2020 Abstract
CIFAR-10
ImageNet
MS COCO
CIFAR-100
MNIST
Oxford 102 Flower
DTD
FGVC-Aircraft
tieredImageNet
Meta-Dataset
Quick, Draw! Dataset
Results from the Paper
Ranked #2 on
Few-Shot Image Classification
on Mini-Imagenet 10-way (5-shot)
(using extra training data)
