【摘要】 深度学习技术在目标检测领域取得了显著的成果，但是相关模型在样本量不足的条件下难以发挥作用，借助小样本学习技术可以解决这一问题。本文提出一种新的小样本目标检测模型。首先，设计了一种特征学习器，由Swin Transformer模块和PANET模块组成，从查询集中提取包含全局信息的多尺度元特征，以检测新的类对象。其次，设计了一种权重调整模块，将支持集转换为一个具有类属性的权重系数，为检测新的类对象调整元特征分布。最后在ImageNet-LOC、PASCAL VOC和COCO三种数据集上进行实验分析，结果表明本文提出的模型在平均精度、平均召回率指标上相对于现有的先进模型都有了显著的提高。更多还原

【Abstract】 Deep learning technology has achieved remarkable results in the field of target detection, but related models are difficult to function under the condition of insufficient sample size.With the help of few-shot learning technology, a new few-shot object detection model is proposed.First, a feature learner is designed, consisting of a Swin Transformer module and a PANET module, to extract multi-scale meta-features containing global information from the query set to detect new class objects. Second, a weight adjustment module is designed to convert the support set into a weight coefficient with class attributes to adjust the meta-feature distribution for detecting new class objects. Finally, experimental analysis is carried out on ImageNet-LOC, PASCAL VOC and COCO datasets. The results show that the model proposed in this paper has a significant improvement in mAP and AR indicators compared to the existing advanced models.更多还原