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Multi-level navigation network: advancing fine-grained visual classification

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Abstract

Fine-grained visual classification (FGVC) is defined as the finer division of sub-categories within basic categories. The task is both valuable and challenging. Its difficulty primarily arises from its intrinsic slight inter-class variations and substantial intra-class differences. The crucial solution to FGVC lies in identifying local regions with subtle yet discriminative features and effectively representing them. Nevertheless, with the increasing prevalence of deep convolutional neural networks, researchers have primarily prioritized the use of high-level, abstract, semantic features to achieve FGVC, consequently overlooking low-level, detailed information, resulting in poor feature representation capabilities. Thus, we put forward the multi-level navigation network, denoted as MLNN, to enhance feature representation by incorporating both high-level semantics and low-level details. Specifically, MLNN is composed of (1) the feature refinement and attention enhancement module, which enables the network to learn detailed feature representations and further enhance features with attention mechanisms, and (2) the triplet-enhanced multi-level fusion module, which integrates the features of different levels, leading to a more comprehensive feature representation. Experimental outcomes reveal that our approach attains state-of-the-art performance on three widely-accepted benchmark datasets.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Some or all of the code used during the study is available on request from the corresponding author.

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Acknowledgements

The authors are very indebted to the anonymous referees for their critical comments and suggestions for the improvement of this paper.

Funding

This work was supported by the National Natural Science Foundation of China (No. 61673396) and the Natural Science Foundation of Shandong Province (No. ZR2022MF260).

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Authors and Affiliations

  1. Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China

    Hong Liang, Xian Li, Mingwen Shao & Qian Zhang

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  1. Hong Liang
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  2. Xian Li
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  3. Mingwen Shao
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  4. Qian Zhang
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Contributions

HL and XL were instrumental in devising the concept; HL, XL, and QZ contributed to the development of the methodology. The software development was overseen by XL and QZ. HL, XL, MS, and QZ conducted the formal analysis. XL was in charge of drafting the initial manuscript. HL and MS participated in revising the manuscript and provided editorial input, secured funding, and supervised the project. Additionally, HL, MS, and QZ provided the necessary resources for the study.

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Correspondence to Xian Li.

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Liang, H., Li, X., Shao, M. et al. Multi-level navigation network: advancing fine-grained visual classification. J Supercomput 81, 409 (2025). https://doi.org/10.1007/s11227-025-06933-4

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