To improve the performance of few-shot classification, we present a general and flexible method named Multi-Scale Attention and Domain Adaptation Network (MADA). Firstly, to tackle the problem of limited samples, a masked autoencoder is used to image augmentation. Moreover, it can be inserted as a plug-and-play module into a few-shot classification. Secondly, the multi-scale attention module can adapt feature vectors extracted by embedding function to the current classification task. Multi-scale attention machine strengthens the discriminative image region by focusing on relating samples in both base class and novel class, which makes prototypes more accurate. In addition, the embedding function pays attention to the task-specific feature. Thirdly, the domain adaptation module is used to address the domain shift caused by the difference in data distributions of the two domains. The domain adaptation module consists of the metric module and the margin loss function. The margin loss pushes different prototypes away from each other in the feature space. Sufficient margin space in feature space improves the generalization performance of the method. The experimental results show the classification accuracy of the proposed method is 67.45% for 5-way 1-shot and 82.77% for 5-way 5-shot on the miniImageNet dataset. The classification accuracy is 70.57% for 5-way 1-shot and 85.10% for 5-way 5-shot on the tieredImageNet dataset. The classification accuracy of our method is better than most previous methods. After dimension reduction and visualization of features by using t-SNE, it can be concluded that domain drift is alleviated, and prototypes are more accurate. The multi-scale attention module enhanced feature representations are more discriminative for the target classification task. In addition, the domain adaptation module improves the generalization ability of the model.

针对少量样本条件下模型易过拟合、目标错检与漏检问题，本文基于TFA (two-stage fine-tuning approach)提出了一种在线推断校准的小样本目标检测框架。该框架设计了一种全新的Attention-FPN网络，通过建模特征通道间的依赖关系选择性融合特征，结合分级冻结的学习机制引导RPN模块提取正确的新类前景目标；同时，构建了一种在线校准模块对样本进行实例分割编码，对众多候选目标进行评分重加权处理，纠正误检和漏检的预测目标。结果表明，所提算法在VOC数据集Novel Set1中，五个任务的平均nAP50提升10.16%，在性能上优于目前的主流算法。