融合扩充-双重特征提取应用于小样本学习

小样本图片分类的目标是根据极少数带有标注的样本去识别该类别, 其中两个关键问题是带标注的数据量过少和不可见类别(训练类别和测试类别的不一致). 针对这两个问题, 我们提出了一个新的小样本分类模型: 融合扩充-双重特征提取模型. 首先, 我们引入了一个融合扩充机制(FE), 这个机制利用可见类别样本中同一类别不同样本之间的变化规则, 对支持集的样本进行扩充, 从而增加支持集中的样本数量, 使提取的特征更具鲁棒性. 其次, 我们提出了一种双重特征提取机制(DF), 该机制首先利用基类的大量数据训练两个不同的特征提取器: 局部特征提取器和整体特征提取器, 利用两个不同的特征提取器对样本特征进行提取, 使提取的特征更加全面, 然后根据局部和整体特征对比, 突出对分类影响最大的特征, 从而提高分类准确性. 在Mini-ImageNet和Tiered-ImageNet数据集上, 我们的模型都取得了较好的效果.

Fusion Expansion-dual Feature Extraction Applied to Few-shot Learning

The goal of few-shot image classification is to identify the category based on a very small number of labeled samples. Two of the key issues are too little labeled data and invisible categories (the training category and the test category are inconsistent). In response, we propose a new few-shot classification model: fusion expansion-dual feature extraction model. First, we introduce a fusion expansion mechanism (FE), which uses the change rules between different samples of the same category in the visible category samples to expand the support set samples, thereby increasing the number of samples in the support set and making the extracted features more robust. Secondly, we propose a dual feature extraction mechanism (DF). A large amount of data from the base class is first utilized to train two different feature extractors: a local feature extractor and a global feature extractor, which are applied to extract more comprehensive sample features. Then the local and overall features are compared to highlight the features that have the greatest impact on the classification, thereby improving the accuracy of the classification. On the Mini-ImageNet and Tiered-ImageNet datasets, our model has achieved good results.