Abstract: Fine-grained visual categorization is an important but challenging task in computer vision due to high intraclass and low inter-class variance. Classical fine-grained image recognition methods use a single-input with single-output approach, which limits the ability of the model to learn inference from paired images. Inspired by the behavior of human beings when discriminating fine-grained images, a deep pairwise feature comparison interactive fine-grained classification algorithm (PCI) is proposed to find common or different features between image pairs and effectively improve the fine-grained recognition accuracy. Firstly, PCI establishes a positive-negative pair input strategy to extract pairwise depth features of fine-grained images. Secondly, a deep pairwise feature interaction mechanism is established to realize global information learning, depth comparison and depth adaptive interaction of paired depth features. Finally, a pairwise feature contrastive learning mechanism is established to constrain pairwise deep fine-grained features through contrastive learning, increasing the similarity between positive pairs and reducing the similarity between negative pairs. Extensive experiments are conducted on the popular fine-grained datasets CUB-200-2011, Stanford Dogs, Stanford Cars, and FGVC-Aircraft, and the experimental results show that PCI outperforms current state-of-the-art methods.

Key words: fine-grained, image classification, deep neural network, contrastive learning, attention mechanism

摘要： 由于高类内和低类间方差，细粒度图像识别成为计算机视觉领域一项极具挑战性的研究课题。经典的细粒度图像识别方法采用单输入单输出的方式，限制了模型从成对图像中对比学习推理的能力。受人类在判别细粒度图像时的行为启发，提出了深度成对特征对比交互细粒度分类算法（PCI），深度对比寻找图像对之间的共同、差异特征，有效提升细粒度识别精度。首先，PCI建立正负对输入策略，提取细粒度图像的成对深度特征；其次，建立深度成对特征交互机制，实现成对深度特征的全局信息学习、深度对比以及深度自适应交互；最后，建立成对特征对比学习机制，通过对比学习约束成对深度细粒度特征，增大正对之间的相似性并减小负对之间的相似性。在流行的细粒度数据集CUB-200-2011、Stanford Dogs、Stanford Cars以及FGVC-Aircraft上开展了广泛的实验，实验结果表明PCI的性能优于当前最先进的方法。

关键词: 细粒度, 图像分类, 深度神经网络, 对比学习, 注意力机制