国家自然科学基金地区项目(62061049); 云南省应用基础研究计划重点项目(202001BB050032); 云南省应用基础研究计划面上项目(2018FB100)
野生蛇的分类相较于其他细粒度图像分类更加困难和复杂, 这是因为蛇姿势各异、变化急促、常处于运动或盘曲状态, 很难根据蛇的局部特征去判断并分类. 为了解决这个问题, 本文将自注意力机制应用野生蛇细粒度图像分类, 从而解决卷积神经网络因层数加深造成的过于专注局部而忽略全局信息问题. 通过Swin Transformer (Swin-T)进行迁移学习获得细粒度特征提取模型. 为了进一步研究自注意力机制在元学习领域的性能, 本文改进特征提取模型搭建孪生网络并构造元学习器对少量样本进行学习和分类. 相较于其他方法, 本方法减少了元学习在特征提取时所造成的时间和空间开销, 提高了元学习分类的准确率和效率并增加了元学习的自主学习性.
Compared with other fine-grained image classifications, that of wild snakes is more difficult and complicated, as it is difficult to judge and classify snakes by their local characteristics due to their different postures, rapid posture changes, and usual status of motion or coiling. In response, this study applies the self-attention mechanism to fine-grained wild snake image classification to solve the problem that the convolutional neural network focuses too much on the local parts to ignore the global information due to the increasing number of layers. Transfer learning is implemented through Swin Transformer (Swin-T) to obtain a fine-grained feature extraction model. To further study the performance of the self-attention mechanism in meta-learning, this study improves the feature extraction model, builds a Siamese network, and construct a meta-learner to learn and classify a small number of samples. Compared with other methods, the proposed method reduces the time and space consumption caused by feature extraction, improves the accuracy and efficiency of meta-learning classification, and increases the learning autonomy of meta-learning.