Abstract
Fine-Grained Visual classification (FGVC) is a fundamental problem in computer vision. FGVC is determined by subtle appearance difference of local parts, which thus inspires many part-based methods. Different from attention-based methods and other part-based methods, we propose a novel Category Probability Mask Learning (CPML) module to discover nuanced local differences and mitigate cluttered backgrounds. Meanwhile, the CPML is a simple and efficient module, which can be applied to both convolution neural networks and vision transformers to enhance the ability of feature representation. In addition, we utilize a Category Consistency Loss (CCL) to promote the robustness and discrimination of learned backbone deep features. It is worth mentioning that we only use global branch at test stage because the global feature is already regularized by the part features with CPML and CCL in training steps. Compared with current state-of-the-art methods, our methods achieve promising performance on three widely used FGVC datasets.
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Acknowledgments
This work was supported in part by The National Natural Science Foundation of China (62202061;62171043); Beijing Natural Science Foundation (4232025); R&D Program of Beijing Municipal Education Commission (KM202311232002); Scientific Research Project of National Language Commission (ZDI145-10).
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Teng, S., Mei, C., You, X., Lyu, X. (2023). CPML: Category Probability Mask Learning for Fine-Grained Visual Classification. In: Yongtian, W., Lifang, W. (eds) Image and Graphics Technologies and Applications. IGTA 2023. Communications in Computer and Information Science, vol 1910. Springer, Singapore. https://doi.org/10.1007/978-981-99-7549-5_12
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DOI: https://doi.org/10.1007/978-981-99-7549-5_12
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