修复局部描述子网络的小样本学习方法

Restore local descriptors network for few-shot learning

针对现有的基于局部描述子的小样本度量学习方法未能考虑局部描述子之间的关联性以及未充分利用类别的全局特征信息的问题,提出了修复局部描述子网络(RLDN).相邻GCN模块通过利用同张图像内的空间位置关系增强局部描述子之间的联系,修复了部分背景噪声局部描述子.全局特征提取模块通过学习并融合图像的全局特征输出类别的全局描述子,再串接局部描述子对其作进一步修复.此外还引入了三元组损失,将其融入到传统的交叉熵损失中提出了全新的混合损失函数,增大了不同类别的间距,有助于分类器减少错误分类的情况.实验结果表明,与传统的局部描述子方法对比,修复局部描述子网络能降低噪声特征对分类器的干扰,有效提升模型的分类准确率.

Aiming at the problem that the existing few-shot metric learning methods based on local descriptors fail to consider the correlation between local descriptors and fail to make full use of the global feature information of categories,this paper proposes the Restore Local Descriptors Network(RLDN in short).The adjacent GCN module increases the relevance between local descriptors by using the spatial position relation in the same image.The global feature extraction module outputs the global descriptors of the category by learning and fusing the global features of the image,and then concatenates the local descriptors for further restoration.In addition,a new hybrid loss function is proposed by introducing the triple loss which is integrated into the traditional cross entropy loss.It increases the distance between different categories and helps the classifier to reduce the misclassification.The experimental results show that compared with the traditional local descriptor methods,the Restore Local Descriptors Network can reduce the interference of noise features on the classifier and effectively improve the classification accuracy of the model.