细粒度图像分类的通道自适应判别性学习方法

由于类内差异大且类间差异小，因此细粒度图像分类极具挑战性。鉴于深层特征具有很强的特征表示能力，而中层特征又能有效地补充全局特征在图像细粒度识别中的缺失信息，因此，为了充分利用卷积层的特征，本文提出细粒度图像分类的通道自适应判别性学习方法：首先在通道方向上聚集中级特征以获取目标位置；然后对通过感兴趣区域特征交互级联得到的信息进行分类；最后进行端到端的训练，无需任何边界框和零件注释。在CUB-200-2011、Stanford Cars和FGVC-Aircraft这3个公共数据集上开展大量实验，与其他方法相比，本文方法既可以保持简单性和推理效率又可提升分类准确度。

Fine-grained Image Classification via Channel Adaptive Discriminative Learning

Fine-grained image classification is very challenging due to the limited amount of data with large intra-class differences and small inter-class differences. Since the deep features have strong feature representation capability and the middle layer features can effectively supplement the missing information of the global-level features in fine-grained image identification, in order to take advantages of the convolutional layer feature, this paper proposes a channel adaptive discriminative learning method for fine-grained image classification. In this method, the intermediate features are first aggregated in the channel direction to capture the target position, and then we classify the information obtained by the interactive cascade of the region of interest features. Finally, the proposed method can perform end-to-end training without any bounding box and part annotation. A large number of experiments on three common fine-grained image classification datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft) have shown that this method can not only maintain simple and reasonable efficiency, but also improve the accuracy, compared with the other methods.