Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions

• URL: http://arxiv.org/abs/2505.23031v1
• Date: Thu, 29 May 2025 03:18:25 GMT
• Title: Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions
• Authors: Jinyi Chang, Dongliang Chang, Lei Chen, Bingyao Yu, Zhanyu Ma,
• Abstract summary: This paper aims to enable accurate fine-grained recognition without direct access to instance labels.<n>Unlike existing LLP-based methods, our framework explicitly exploits the hierarchical nature of fine-grained datasets.
• Score: 25.974006393027228
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, Fine-Grained Visual Classification (FGVC) has achieved impressive recognition accuracy, despite minimal inter-class variations. However, existing methods heavily rely on instance-level labels, making them impractical in privacy-sensitive scenarios such as medical image analysis. This paper aims to enable accurate fine-grained recognition without direct access to instance labels. To achieve this, we leverage the Learning from Label Proportions (LLP) paradigm, which requires only bag-level labels for efficient training. Unlike existing LLP-based methods, our framework explicitly exploits the hierarchical nature of fine-grained datasets, enabling progressive feature granularity refinement and improving classification accuracy. We propose Learning from Hierarchical Fine-Grained Label Proportions (LHFGLP), a framework that incorporates Unrolled Hierarchical Fine-Grained Sparse Dictionary Learning, transforming handcrafted iterative approximation into learnable network optimization. Additionally, our proposed Hierarchical Proportion Loss provides hierarchical supervision, further enhancing classification performance. Experiments on three widely-used fine-grained datasets, structured in a bag-based manner, demonstrate that our framework consistently outperforms existing LLP-based methods. We will release our code and datasets to foster further research in privacy-preserving fine-grained classification.

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