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

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