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.

翻译：少样本图像分类旨在仅利用少量标记样本准确分类未标记图像。现有最优解决方案基于深度学习构建，专注于设计日益复杂的深度骨干网络。然而，由于难以将训练类别中学到的知识迁移到新类别，该任务仍面临巨大挑战。本文提出一种基于非独立同分布渐进式机器学习（GML）范式的新方法。该方法从少量标记观测数据出发，通过因子图中的迭代因子推理，按照难度递增顺序逐步标记目标图像。具体而言，我们提出的方案通过深度骨干网络提取指示性特征表示，然后基于提取的特征构建一元因子和二元因子以促进渐进学习。一元因子基于嵌入空间中的类别中心距离构建，而二元因子则基于k近邻构建。我们通过对比研究，在基准数据集上实证验证了所提方法的性能。大量实验表明，该方法可将准确率提升当前最优性能的1-5%。更重要的是，该方法比现有深度模型具有更强的鲁棒性：当查询集规模增大时，其性能持续提升，而深度模型的性能基本持平甚至下降。