Exploration of Class Center for Fine-Grained Visual Classification
<|reference_start|>Exploration of Class Center for Fine-Grained Visual Classification: Different from large-scale classification tasks, fine-grained visual classification is a challenging task due to two critical problems: 1) evident intra-class variances and subtle inter-class differences, and 2) overfitting owing to fewer training samples in datasets. Most existing methods extract key features to reduce intra-class variances, but pay no attention to subtle inter-class differences in fine-grained visual classification. To address this issue, we propose a loss function named exploration of class center, which consists of a multiple class-center constraint and a class-center label generation. This loss function fully utilizes the information of the class center from the perspective of features and labels. From the feature perspective, the multiple class-center constraint pulls samples closer to the target class center, and pushes samples away from the most similar nontarget class center. Thus, the constraint reduces intra-class variances and enlarges inter-class differences. From the label perspective, the class-center label generation utilizes classcenter distributions to generate soft labels to alleviate overfitting. Our method can be easily integrated with existing fine-grained visual classification approaches as a loss function, to further boost excellent performance with only slight training costs. Extensive experiments are conducted to demonstrate consistent improvements achieved by our method on four widely-used fine-grained visual classification datasets. In particular, our method achieves state-of-the-art performance on the FGVC-Aircraft and CUB-200-2011 datasets.<|reference_end|>
@article{yao2024exploration,
    title={Exploration of Class Center for Fine-Grained Visual Classification},
    author={Hang Yao, Qiguang Miao, Peipei Zhao, Chaoneng Li, Xin Li, Guanwen Feng
  and Ruyi Liu},
    journal={arXiv preprint arXiv:2407.04243},
    year={2024},
    doi={10.1109/TCSVT.2024.3406443},
    archivePrefix={arXiv},
    eprint={2407.04243},
    primaryClass={cs.CV}
}