{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T04:18:34Z","timestamp":1788236314986,"version":"build-2803163510"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015397","name":"Hohhot Basic Research and Applied Basic Research Project","doi-asserted-by":"publisher","award":["2024-Gui-Ji-33"],"award-info":[{"award-number":["2024-Gui-Ji-33"]}],"id":[{"id":"10.13039\/501100015397","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Inner Mongolia Autonomous Region Science and Technology Plan Project","award":["2025YFSH0137"],"award-info":[{"award-number":["2025YFSH0137"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Fine-grained visual classification (FGVC) aims to distinguish highly similar subcategories, and its performance relies heavily on the accurate modeling of discriminative local parts and their structural relationships. However, existing Vision Transformer-based methods are susceptible to background noise interference, and the relationship modeling approach relying on explicit spatial coordinates struggles to maintain stable structural representations when targets undergo pose variations and non-rigid deformations. To address these issues, this paper proposes a Semantic Topology Part Network (STP-Net). First, a Prior-Guided Part Aggregator (PGA) is designed, which leverages the foreground prior provided by foundation models to guide discriminative part discovery, enhancing target region responses while suppressing background interference. Second, a Topology-Informed Semantic Graph Convolutional Network (TIS-GCN) is designed to dynamically construct topological relationships among parts in an implicit semantic space, achieving robust modeling against complex structural variations. Furthermore, a Semantic\u2013Spatial Cross-Attention (SSCA) mechanism is introduced to establish bidirectional interaction between semantic relationships and spatial features, and combined with a Global-Context Adaptive Gating mechanism to accomplish multi-scale feature fusion. On four mainstream fine-grained visual classification benchmarks, namely CUB-200-2011, Stanford Cars, Stanford Dogs, and NABirds, the proposed model achieves Top-1 accuracies of 92.7%, 94.9%, 95.2%, and 92.3%, respectively. Comprehensive ablation studies and visualization analyses further validate the effectiveness of the proposed method in background suppression, structural relationship modeling, and discriminative feature learning.<\/jats:p>","DOI":"10.3390\/jimaging12090407","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:27:46Z","timestamp":1787920066000},"page":"407","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantic Topological Multi-Scale Part Network for Fine-Grained Visual Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Xuerong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Min","family":"Zhi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Yanjun","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Rula","family":"Sa","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55982","DOI":"10.1109\/ACCESS.2026.3681899","article-title":"Fine-grained Visual Classification: A Survey of Methods, Architectures, and Emerging Trends","volume":"14","author":"Jung","year":"2026","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"Chou, P.Y., Kao, Y.Y., and Lin, C.H. (2023). Fine-grained visual classification with high-temperature refinement and background suppression. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sun, H., He, X., and Peng, Y. (2022, January 10\u201314). Sim-trans: Structure information modeling transformer for fine-grained visual categorization. Proceedings of the 30th ACM International Conference on Multimedia, Lisboa, Portugal.","DOI":"10.1145\/3503161.3548308"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023, January 1\u20136). Segment Anything. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, N., Donahue, J., Girshick, R., and Darrell, T. (2014, January 6\u201312). Part-based R-CNNs for fine-grained category detection. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_54"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1016\/j.patcog.2017.10.002","article-title":"Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization","volume":"76","author":"Wei","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Branson, S., Van Horn, G., Belongie, S., and Perona, P. (2014). Bird species categorization using pose normalized deep convolutional nets. arXiv.","DOI":"10.5244\/C.28.87"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fu, J., Zheng, H., and Mei, T. (2017, January 21\u201326). Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.476"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., and Wang, L. (2018, January 8\u201314). Learning to navigate for fine-grained classification. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_26"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, Y., Bai, Y., Zhang, W., and Mei, T. (2019, January 15\u201320). Destruction and construction learning for fine-grained image recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00530"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhuang, P., Wang, Y., and Qiao, Y. (2020, January 7\u201312). Learning attentive pairwise interaction for fine-grained classification. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.7016"},{"key":"ref_12","unstructured":"Luo, W., Yang, X., Mo, X., Lu, Y., Davis, L.S., Li, J., and Lim, S.N. (November, January 27). Cross-x learning for fine-grained visual categorization. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Du, R., Chang, D., Bhunia, A.K., Xie, J., Ma, Z., Song, Y.Z., and Guo, J. (2020, January 23\u201328). Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58565-5_10"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3492221","article-title":"Fine-grained image classification via multi-scale selective hierarchical biquadratic pooling","volume":"18","author":"Tan","year":"2022","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_15","first-page":"852","article-title":"TransFG: A transformer architecture for fine-grained recognition","volume":"36","author":"He","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell. Virtual"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9015","DOI":"10.1109\/TMM.2023.3244340","article-title":"Fine-grained visual classification via internal ensemble learning transformer","volume":"25","author":"Xu","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111224","DOI":"10.1016\/j.patcog.2024.111224","article-title":"LDH-ViT: Fine-grained visual classification through local concealment and feature selection","volume":"161","author":"Shi","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hu, C., Zhu, L., Qiu, W., and Wu, W. (2022). Data augmentation vision transformer for fine-grained image classification. arXiv.","DOI":"10.2139\/ssrn.4063510"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110265","DOI":"10.1016\/j.patcog.2024.110265","article-title":"FET-FGVC: Feature-enhanced transformer for fine-grained visual classification","volume":"149","author":"Chen","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109979","DOI":"10.1016\/j.patcog.2023.109979","article-title":"A vision transformer for fine-grained classification by reducing noise and enhancing discriminative information","volume":"145","author":"Zhang","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jiang, X., Tang, H., Gao, J., Du, X., He, S., and Li, Z. (2024, January 20\u201327). Delving into multimodal prompting for fine-grained visual classification. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i3.28034"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, L., Chen, T., Yin, J., See, S., and Liu, J. (2023, January 1\u20136). Learning gabor texture features for fine-grained recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00156"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111955","DOI":"10.1016\/j.patcog.2025.111955","article-title":"Structural feature enhanced transformer for fine-grained image recognition","volume":"169","author":"Yu","year":"2026","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"132183","DOI":"10.1016\/j.neucom.2025.132183","article-title":"Background noise suppression for advanced fine-grained visual classification","volume":"667","author":"Wang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11263-023-01873-z","article-title":"Accurate fine-grained object recognition with structure-driven relation graph networks","volume":"132","author":"Wang","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Yan, K., Huang, F., and Li, J. (2021, January 20\u201325). Graph-based high-order relation discovery for fine-grained recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01483"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s41095-024-0424-2","article-title":"SAM-driven MAE pre-training and background-aware meta-learning for unsupervised vehicle re-identification","volume":"10","author":"Wang","year":"2024","journal-title":"Comput. Vis. Media"},{"key":"ref_28","unstructured":"Yin, S., and Peng, Y. (2026, August 12). Part-Aware CLIP: Enhancing Fine-Grained Understanding with Part-level Descriptions. OpenReview 2026, c1VgleBVlw. Available online: https:\/\/openreview.net\/forum?id=c1VgleBVlw."},{"key":"ref_29","unstructured":"Amir, S., Gandelsman, Y., Bagon, S., and Dekel, T. (2021). Deep vit features as dense visual descriptors. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4529","DOI":"10.1109\/TIP.2024.3441813","article-title":"Multi-granularity part sampling attention for fine-grained visual classification","volume":"33","author":"Wang","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. (2011). The Caltech-UCSD Birds-200-2011 Dataset, California Institute of Technology. Technical Report CNS-TR-2011-001."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., and Fei-Fei, L. (2013, January 1\u20138). 3D object representations for fine-grained categorization. Proceedings of the IEEE International Conference on Computer Vision Workshops, Sydney, Australia.","DOI":"10.1109\/ICCVW.2013.77"},{"key":"ref_33","unstructured":"Khosla, A., Jayadevaprakash, N., Yao, B., and Li, F.F. (2011, January 20\u201325). Novel dataset for fine-grained image categorization: Stanford dogs. Proceedings of the CVPR Workshop on Fine-Grained Visual Categorization (FGVC), Colorado Springs, CO, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., Perona, P., and Belongie, S. (2015, January 7\u201312). Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298658"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, Y., Chen, K., Xu, Y., and Tian, Y. (2022, January 18\u201324). Fine-grained object classification via self-supervised pose alignment. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00725"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, C., Xie, H., Zha, Z.J., Ma, L., Yu, L., and Zhang, Y. (2020, January 7\u201312). Filtration and distillation: Enhancing region attention for fine-grained visual categorization. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6822"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1109\/LSP.2021.3114622","article-title":"Complemental attention multi-feature fusion network for fine-grained classification","volume":"28","author":"Miao","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, J., Yu, X., and Gao, Y. (2021). Feature fusion vision transformer for fine-grained visual categorization. arXiv.","DOI":"10.5244\/C.35.192"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/407\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:54:53Z","timestamp":1788234893000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/407"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,28]]},"references-count":39,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jimaging12090407"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12090407","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,28]]}}}