Abstract
Fine-grained image classification of wildlife species is a task of practical value and has an important role to play in the fields of endangered animal conservation, environmental protection and ecological conservation. However, the small differences between different subclasses of wildlife and the large differences within the same subclasses pose a great challenge to the classification of wildlife species. In addition, the feature extraction capability of existing methods is insufficient, ignoring the role of shallow effective features and failing to identify subtle differences between images well. To solve the above problems, this paper proposes an improved Swin Transformer architecture, called SFRSwin. Specifically, a shallow feature retention mechanism is proposed, where the mechanism consists of a branch that extracts significant features from shallow features, is used to retain important features in the shallow layers of the image, and forms a dual-stream structure with the original network. SFRSwin was trained and tested on the communal dataset Stanford Dogs and the small-scale dataset Shark species, and achieved an accuracy of 93.8\(\%\) and 84.3\(\%\) on the validation set, an improvement of 0.1\(\%\) and 0.3\(\%\) respectively over the pre-improvement period. In terms of complexity, the FLOPs only increased by 2.7\(\%\) and the number of parameters only increased by 0.15\(\%\).
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62272256, the Shandong Provincial Natural Science Foundation under Grants ZR2021MF026 and ZR2023MF040, the Innovation Capability Enhancement Program for Small and Medium-sized Technological Enterprises of Shandong Province under Grants 2022TSGC2180 and 2022TSGC2123, the Innovation Team Cultivating Program of Jinan under Grant 202228093, and the Piloting Fundamental Research Program for the Integration of Scientific Research, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) under Grants 2021JC02014 and 2022XD001, the Talent Cultivation Promotion Program of Computer Science and Technology in Qilu University of Technology (Shandong Academy of Sciences) under Grants 2021PY05001 and 2023PY059.
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Wang, S. et al. (2024). SFRSwin: A Shallow Significant Feature Retention Swin Transformer for Fine-Grained Image Classification of Wildlife Species. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14433. Springer, Singapore. https://doi.org/10.1007/978-981-99-8546-3_19
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DOI: https://doi.org/10.1007/978-981-99-8546-3_19
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