Abstract: To effectively extract product style features with differences,a Fine-grained Styles Recognition Convolutional Neural Network (FSR-CNN) based on composite learning pipelines was proposed,which simulation integrated two key learning mechanisms of the human brain nervous system.From the attention learning pipeline,based on the residual structure,the coordinate attention,convolutional block attention and multi-head attention were embedded in a string-parallel combination to form a lightweight Hybrid Attention Residual Network (HA-ResNet) for extracting “specialized features”.From the transfer learning pipeline,the fine-tuning pre-trained GoogLeNet was used to expand the capacity of HA-ResNet model for extracting multi-receptive field “generic features”.Finally,the output features of both were fused and the MLP classifier was used to identify the product style types.Experiments were performed on a self-built bicycle helmet dataset and compared with other classical deep convolutional neural network models.The experimental results showed that the FSR-CNN model exhibited higher accuracy and stronger robustness,which provided a new model algorithm architecture for product styles fine retrieval and reuse.

Key words: product form, style recognition, hybrid attention, transfer learning, composite learning mechanism

摘要： 为有效提取具有差异性的产品风格特征,提出一种基于复合学习通路的细粒度风格识别卷积神经网络(FSR-CNN)。一是注意力学习通路,以残差结构为基础,采用串并结合的方式将坐标注意力、卷积块注意力和多头注意力嵌入其中,提出轻量化的混合注意力残差网络(HA-ResNet),用于抽取“专用特征”。二是迁移学习通路,应用微调预先训练的GoogLeNet以扩充HA-ResNet模型容量,实现多感受野“通用特征”抽取。最后对二者输出的特征进行融合,并使用MLP分类器识别产品风格类型。在自行车头盔数据集上进行实验,并与其他经典深度卷积神经网络模型进行比较,实验结果表明FSR-CNN模型表现出较高的准确率和良好的稳健性,为产品风格精细检索与知识重用提供了一种新的模型算法架构。

关键词: 产品造型, 风格识别, 混合注意力, 迁移学习, 复合学习机制