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<p>To tackle the challenges of capturing discriminative features and effectively leveraging multi-granularity information in fine-grained image classification, this paper proposes an Interactive Modeling Network with Feature Visibility Optimization (IMN-FVO), based on a visibility-guided and interactive learning strategy. IMN-FVO enhances the utilization of multi-scale features and improves the extraction of subtle discriminative details through comprehensive feature mining. It comprises three key modules: (1) the Feature Visibility Mining Unit, which enhances salient features and suppresses redundant ones to improve feature representation and discrimination; (2) the Strip Convolution Optimization Module, which focuses on precise localization by filtering out irrelevant information; and (3) the Interactive Multi-layer Perceptron Module, which models multi-level feature interactions to enrich semantic representation and fusion. The entire framework is trained end-to-end, without requiring bounding box annotations or multi-stage processing. Experiments on CUB-200–2011, Stanford Cars, and FGVC-Aircraft show that IMN-FVO outperforms existing state-of-the-art methods, demonstrating strong effectiveness and generalization.</p>

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Feature visibility optimization-based interactive modeling for fine-grained image classification

  • 期刊论文

  • 发表日期：

    2025年6月19日

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  • Kai Yang Liao,
  • Yun Fei Tan,
  • Yuan Lin Zheng,
  • Guang Feng Lin,
  • Gang Huang,
  • Ding Wen Song

摘要

To tackle the challenges of capturing discriminative features and effectively leveraging multi-granularity information in fine-grained image classification, this paper proposes an Interactive Modeling Network with Feature Visibility Optimization (IMN-FVO), based on a visibility-guided and interactive learning strategy. IMN-FVO enhances the utilization of multi-scale features and improves the extraction of subtle discriminative details through comprehensive feature mining. It comprises three key modules: (1) the Feature Visibility Mining Unit, which enhances salient features and suppresses redundant ones to improve feature representation and discrimination; (2) the Strip Convolution Optimization Module, which focuses on precise localization by filtering out irrelevant information; and (3) the Interactive Multi-layer Perceptron Module, which models multi-level feature interactions to enrich semantic representation and fusion. The entire framework is trained end-to-end, without requiring bounding box annotations or multi-stage processing. Experiments on CUB-200–2011, Stanford Cars, and FGVC-Aircraft show that IMN-FVO outperforms existing state-of-the-art methods, demonstrating strong effectiveness and generalization.