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Few-shot image classification aims to accurately classify unlabeled images
using only a few labeled samples. The state-of-the-art solutions are built by
deep learning, which focuses on designing increasingly complex deep backbones.
Unfortunately, the task remains very challenging due to the difficulty of
transferring the knowledge learned in training classes to new ones. In this
paper, we propose a novel approach based on the non-i.i.d paradigm of gradual
machine learning (GML). It begins with only a few labeled observations, and
then gradually labels target images in the increasing order of hardness by
iterative factor inference in a factor graph. Specifically, our proposed
solution extracts indicative feature representations by deep backbones, and
then constructs both unary and binary factors based on the extracted features
to facilitate gradual learning. The unary factors are constructed based on
class center distance in an embedding space, while the binary factors are
constructed based on k-nearest neighborhood. We have empirically validated the
performance of the proposed approach on benchmark datasets by a comparative
study. Our extensive experiments demonstrate that the proposed approach can
improve the SOTA performance by 1-5% in terms of accuracy. More notably, it is
more robust than the existing deep models in that its performance can
consistently improve as the size of query set increases while the performance
of deep models remains essentially flat or even becomes worse.
