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Yu Shi

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Proceedings Article | 13 June 2024 Paper

Fine-grained visual classification with channel filter and adaptive graph neural network

Dingli Huang, Yu Shi

Proceedings Volume 13180, 131801O (2024) https://doi.org/10.1117/12.3033171

KEYWORDS: Neural networks, Image classification, Feature fusion, Visualization, Matrices, Data modeling, Semantics, Performance modeling

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Fine-grained classification poses significant challenges due to high intra-class variability and high inter-class similarity, making coarse-grained classification methods inadequate. Previous research has focused on locating objects in images while overlooking their detailed features. Additionally, images contain not only targets but also redundant information such as background and noise. The relationships between extracted features are also crucial, a point often overlooked in previous studies. To address these challenges, this paper proposes the Multi-scale Graph Neural Network Filter (MGF) model, composed of two modules: the Filter High-Resolution Feature Pyramid Network (FHRFPN) and the Adaptive Graph Neural Network (AGNN). FHRFPN progressively fuses features of different scales to retain rich detail information while avoiding conflicts between information and locating target positions. Before outputting, it employs the Channel Filter (CF) block to filter out noise features and reduce unnecessary computational overhead. AGNN adaptively creates adjacency matrices between features based on the input features. Through graph neural networks, the model comprehensively considers the interactions between features, effectively capturing non-linear and complex dependencies. The proposed method achieves state-of-the-art performance on the NA-Birds and CUB-200-2011 benchmarks, thus providing a promising solution for enhancing the performance of fine-grained visual classification tasks.

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