Few-shot learning (FSL), purposing to resolve the problem of data-scarce, has attracted considerable attention in recent years. A popular FSL framework contains two phases: (i) the pre-train phase employs the base data to train a CNN-based feature extractor. (ii) the meta-test phase applies the frozen feature extractor to novel data (novel data has different categories from base data) and designs a classifier for recognition. To correct few-shot data distribution, researchers propose Semi-Supervised Few-Shot Learning (SSFSL) by introducing unlabeled data. Although SSFSL has been proved to achieve outstanding performances in the FSL community, there still exists a fundamental problem: the pre-trained feature extractor can not adapt to the novel data flawlessly due to the cross-category setting. Usually, large amounts of noises are introduced to the novel feature. We dub it as Feature-Extractor-Maladaptive (FEM) problem. To tackle FEM, we make two efforts in this paper. First, we propose a novel label prediction method, Isolated Graph Learning (IGL). IGL introduces the Laplacian operator to encode the raw data to graph space, which helps reduce the dependence on features when classifying, and then project graph representation to label space for prediction. The key point is that: IGL can weaken the negative influence of noise from the feature representation perspective, and is also flexible to independently complete training and testing procedures, which is suitable for SSFSL. Second, we propose Graph Co-Training (GCT) to tackle this challenge from a multi-modal fusion perspective by extending the proposed IGL to the co-training framework. GCT is a semi-supervised method that exploits the unlabeled samples with two modal features to crossly strengthen the IGL classifier.

翻译：小样本学习旨在解决数据稀缺问题，近年来受到广泛关注。典型的小样本学习框架包含两个阶段：(i) 预训练阶段利用基类数据训练基于CNN的特征提取器；(ii) 元测试阶段将冻结参数的特征提取器应用于新类数据（其类别与基类不同），并设计分类器进行识别。为修正小样本数据分布，研究者通过引入无标注数据提出半监督小样本学习。尽管半监督小样本学习已被证明在小样本学习领域取得显著性能，但仍存在根本性问题：由于跨类别设置，预训练特征提取器无法完美适配新类数据，通常会产生大量噪声污染新类特征，我们将此称为特征提取器失配问题。为解决该问题，本文做出两项贡献：首先，提出新型标签预测方法——孤立图学习。IGL引入拉普拉斯算子将原始数据编码至图空间，有助于在分类时降低对特征的依赖性，进而将图表示投影至标签空间进行预测。关键在于：IGL可从特征表示层面削弱噪声的负面影响，且能灵活独立完成训练与测试流程，适用于半监督小样本学习场景。其次，我们提出图协同训练方法，通过将所提IGL扩展至协同训练框架，从多模态融合视角应对该挑战。GCT是一种半监督方法，利用双模态特征的无标注样本交叉强化IGL分类器。