A Few-shot Learning algorithm based on attention adaptive mechanism

Abstract

Deep learning has achieved remarkable results in image recognition, semantic segmentation and other fields. However, the primary premise of training deep learning model is the support of a large number of data sets and labeled samples. Therefore, deep learning is still faced with difficulties in medical, military and other areas where high-quality samples are scarce. Based on the image classification task as the application background, this paper improves a measure learning algorithm based on covariance representation. In the original Few-shot Learning algorithm based on measure learning, Firstly, from the perspective of second-order statistics, the covariance matrix between the feature vectors of each sample is constructed to realize the class representation; Then, an attention adaptive module is introduced to adjust the feature vectors of the class representation and query samples to make them closer to the class representation of the corresponding class. Finally, the experiment is carried out on the public data set(Omnight) to verify the effectiveness of the model. The experimental results show that the classification accuracy of the model designed in this paper is 6% higher than that of the Few-shot learning algorithm based on metric learning.