Fine-Grained Visual Categorization: Deep Pairwise Feature Comparison Interaction Algorithm

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.