{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T02:00:12Z","timestamp":1772848812613,"version":"3.50.1"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Fine-grained image classification tasks face challenges such as difficulty in labeling, scarcity of samples, and small category differences. To address this problem, this study proposes a novel fine-grained image classification method based on the MogaNet network and a multi-level gating mechanism. A feature extraction network based on MogaNet is constructed, and multi-scale feature fusion is combined to fully mine image information. The contextual information extractor is designed to align and filter more discriminative local features using the semantic context of the network, thereby strengthening the network\u2019s ability to capture detailed features. Meanwhile, a multi-level gating mechanism is introduced to obtain the saliency features of images. A feature elimination strategy is proposed to suppress the interference of fuzzy class features and background noise. A loss function is designed to constrain the elimination of fuzzy class features and classification prediction. Experimental results demonstrate that the new method can be applied to 5-shot tasks across four public datasets: Mini-ImageNet, CUB-200-2011, Stanford Dogs, and Stanford Cars. The accuracy rates reach 79.33, 87.58, 79.34, and 83.82%, respectively, which shows better performance than other state-of-the-art image classification methods.<\/jats:p>","DOI":"10.3389\/fnbot.2025.1630281","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T05:32:15Z","timestamp":1754458335000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Fine-grained image classification using the MogaNet network and a multi-level gating mechanism"],"prefix":"10.3389","volume":"19","author":[{"given":"Dahai","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Su","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"ref1","author":"Chen","year":"2021"},{"key":"ref2","doi-asserted-by":"publisher","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 Recogn."},{"key":"ref3","first-page":"153","volume-title":"Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. European conference on computer vision","author":"Du","year":"2020"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","article-title":"Sigmoid-weighted linear units for neural network function approximation in reinforcement learning","volume":"107","author":"Elfwing","year":"2018","journal-title":"Neural Netw."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TITB.2007.912179","article-title":"Unifying statistical classification and geodesic active regions for segmentation of cardiac MRI","volume":"12","author":"Folkesson","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref6","author":"Ge","year":"2019"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"5994","DOI":"10.1109\/CVPR.2017.775","article-title":"Fine-grained image classification via combining vision and language","author":"He","year":"2017","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"109997","DOI":"10.1016\/j.asoc.2023.109997","article-title":"Fine-grained image analysis for facial expression recognition using deep convolutional neural networks with bilinear pooling","volume":"134","author":"Hossain","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.2298\/CSIS221228034J","article-title":"Heterogenous-view occluded expression data recognition based on cycle-consistent adversarial network and K-SVD dictionary learning under intelligent cooperative robot environment","volume":"20","author":"Jiang","year":"2023","journal-title":"Comput. Sci. Inf. Syst."},{"key":"ref10","author":"Kang","year":"2021"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"109305","DOI":"10.1016\/j.patcog.2023.109305","article-title":"Granularity-aware distillation and structure modeling region proposal network for fine-grained image classification","volume":"137","author":"Ke","year":"2023","journal-title":"Pattern Recogn."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.neucom.2020.02.101","article-title":"Fine-grained vehicle type detection and recognition based on dense attention network","volume":"399","author":"Ke","year":"2020","journal-title":"Neurocomputing"},{"key":"ref13","author":"Khosla","year":"2011"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.1805.07932","article-title":"Bilinear attention networks","volume":"31","author":"Kim","year":"2018","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"ref15","author":"Li","year":"2023"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/s10044-024-01317-5","article-title":"Attention-based supervised contrastive learning on fine-grained image classification","volume":"27","author":"Li","year":"2024","journal-title":"Pattern. Anal. Applic."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1603.06765","article-title":"Fully convolutional attention networks for fine-grained recognition","author":"Liu","year":"2016","journal-title":"arxiv"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1109\/TIP.2021.3135477","article-title":"Cross-part learning for fine-grained image classification","volume":"31","author":"Liu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"104201","DOI":"10.1016\/j.cviu.2024.104201","article-title":"MT-DSNet: mix-mask teacher-student strategies and dual dynamic selection plug-in module for fine-grained image recognition","volume":"249","author":"Lu","year":"2024","journal-title":"Comput. Vis. Image Underst."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1109\/TMM.2023.3279990","article-title":"Deep progressive asymmetric quantization based on causal intervention for fine-grained image retrieval","volume":"26","author":"Ma","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s44147-023-00186-9","article-title":"Few-shot image classification algorithm based on attention mechanism and weight fusion","volume":"70","author":"Meng","year":"2023","journal-title":"J. Eng. Appl. Sci."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1046\/j.1365-246x.2001.00324.x","article-title":"The fourier transform of controlled-source time-domain electromagnetic data by smooth spectrum inversion","volume":"144","author":"Mitsuhata","year":"2001","journal-title":"Geophys. J. Int."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"14799","DOI":"10.1007\/s11042-022-13619-z","article-title":"Learning enhanced features and inferring twice for fine-grained image classification","volume":"82","author":"Nie","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.1805.10123","article-title":"Tadam: task dependent adaptive metric for improved few-shot learning","volume":"31","author":"Oreshkin","year":"2018","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"033026","DOI":"10.1117\/1.JEI.34.3.033026","article-title":"LCNet-ViT-FG: a product recognition method based on the fusion of self-supervised pretrained CNN and transformer","volume":"34","author":"Shen","year":"2025","journal-title":"J. Electron. Imaging"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582688","article-title":"A comprehensive survey of few-shot learning: evolution, applications, challenges, and opportunities","volume":"55","author":"Song","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref27","author":"Wan","year":"2021"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"8335","DOI":"10.1007\/s12652-021-03599-7","article-title":"Aggregate attention module for fine-grained image classification","volume":"14","author":"Wang","year":"2023","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2107.02341","article-title":"Feature fusion vision transformer for fine-grained visual categorization","author":"Wang","year":"2021","journal-title":"arxiv"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"6116","DOI":"10.1109\/TIP.2019.2924811","article-title":"Piecewise classifier mappings: learning fine-grained learners for novel categories with few examples","volume":"28","author":"Wei","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"2821","DOI":"10.1609\/aaai.v37i3.25383","article-title":"Bi-directional feature reconstruction network for fine-grained few-shot image classification","volume":"37","author":"Wu","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref32","doi-asserted-by":"publisher","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. Multimedia"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"21969","DOI":"10.48550\/arXiv.2008.08290","article-title":"Attribute prototype network for zero-shot learning","volume":"33","author":"Xu","year":"2020","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"17444","DOI":"10.1007\/s10489-022-04413-3","article-title":"Discriminant space metric network for few-shot image classification","volume":"53","author":"Yan","year":"2023","journal-title":"Appl. Intell."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"29402","DOI":"10.1109\/JIOT.2024.3353337","article-title":"An anomaly detection model based on deep auto-encoder and capsule graph convolution via sparrow search algorithm in 6G internet-of-everything","volume":"11","author":"Yin","year":"","journal-title":"IEEE Internet Things J."},{"key":"ref36","first-page":"13","article-title":"Data visualization analysis based on explainable artificial intelligence: a survey","volume":"2","author":"Yin","year":"","journal-title":"IJLAI Trans. Sci. Eng."},{"key":"ref37","author":"Yin","year":"2022"},{"key":"ref38","author":"Yuan","year":"2021"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1811.12649","article-title":"Classification is a strong baseline for deep metric learning","author":"Zhai","year":"2018","journal-title":"arxiv"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.knosys.2016.09.027","article-title":"A novel multi-scale cooperative mutation fruit fly optimization algorithm","volume":"114","author":"Zhang","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref41","author":"Zhang","year":"2024"},{"key":"ref42","author":"Zhang","year":"2021"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"110158","DOI":"10.1016\/j.patcog.2023.110158","article-title":"Re-abstraction and perturbing support pair network for few-shot fine-grained image classification","volume":"148","author":"Zhang","year":"2024","journal-title":"Pattern Recogn."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3352418","article-title":"TransFG: a cross-view geo-localization of satellite and UAVs imagery pipeline using transformer-based feature aggregation and gradient guidance","volume":"62","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref45","author":"Zhong","year":"2022"}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2025.1630281\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T05:32:17Z","timestamp":1754458337000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2025.1630281\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":45,"alternative-id":["10.3389\/fnbot.2025.1630281"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2025.1630281","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,6]]},"article-number":"1630281"}}