{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T13:16:03Z","timestamp":1783170963987,"version":"3.54.6"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XTR042021005"],"award-info":[{"award-number":["XTR042021005"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2023ZD0121300"],"award-info":[{"award-number":["2023ZD0121300"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2022JC-41"],"award-info":[{"award-number":["2022JC-41"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.patcog.2026.113826","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T06:49:05Z","timestamp":1776926945000},"page":"113826","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"PD","title":["Frequency-guided generalizable representation learning for cross-domain few-shot learning"],"prefix":"10.1016","volume":"179","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1724-7862","authenticated-orcid":false,"given":"Siqi","family":"Hui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanping","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4616-3318","authenticated-orcid":false,"given":"Ye","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8636-044X","authenticated-orcid":false,"given":"Wenli","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9434-0617","authenticated-orcid":false,"given":"Jinjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.patcog.2026.113826_b1","first-page":"5149","article-title":"Meta-learning in neural networks: A survey","volume":"44","author":"Hospedales","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113826_b2","series-title":"Wave-san: Wavelet based style augmentation network for cross-domain few-shot learning","author":"Fu","year":"2022"},{"key":"10.1016\/j.patcog.2026.113826_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109811","article-title":"Cross-Domain Few-Shot classification via class-shared and class-specific dictionaries","volume":"144","author":"Xu","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113826_b4","doi-asserted-by":"crossref","unstructured":"Y. Fu, Y. Fu, Y.-G. Jiang, Meta-fdmixup: Cross-domain few-shot learning guided by labeled target data, in: Proceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 5326\u20135334.","DOI":"10.1145\/3474085.3475655"},{"key":"10.1016\/j.patcog.2026.113826_b5","doi-asserted-by":"crossref","unstructured":"J. Oh, S. Kim, N. Ho, J.-H. Kim, H. Song, S.-Y. Yun, ReFine: Re-randomization before fine-tuning for cross-domain few-shot learning, in: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 4359\u20134363.","DOI":"10.1145\/3511808.3557681"},{"key":"10.1016\/j.patcog.2026.113826_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128956","article-title":"Meta channel masking for cross-domain few-shot image classification","volume":"620","author":"Hui","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.patcog.2026.113826_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111382","article-title":"CDCNet: Cross-domain few-shot learning with adaptive representation enhancement","volume":"162","author":"Li","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113826_b8","doi-asserted-by":"crossref","unstructured":"L. Zhuo, Y. Fu, J. Chen, Y. Cao, Y.-G. Jiang, Tgdm: Target guided dynamic mixup for cross-domain few-shot learning, in: Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 6368\u20136376.","DOI":"10.1145\/3503161.3548052"},{"key":"10.1016\/j.patcog.2026.113826_b9","doi-asserted-by":"crossref","unstructured":"Y. Fu, Y. Xie, Y. Fu, J. Chen, Y.-G. Jiang, Me-d2n: Multi-expert domain decompositional network for cross-domain few-shot learning, in: Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 6609\u20136617.","DOI":"10.1145\/3503161.3547995"},{"key":"10.1016\/j.patcog.2026.113826_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112548","article-title":"Gradient-guided channel masking for cross-domain few-shot learning","volume":"305","author":"Hui","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.patcog.2026.113826_b11","article-title":"FDGNet: Frequency disentanglement and data geometry for domain generalization in cross-scene hyperspectral image classification","author":"Qin","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2026.113826_b12","doi-asserted-by":"crossref","unstructured":"J. Guo, N. Wang, L. Qi, Y. Shi, Aloft: A lightweight mlp-like architecture with dynamic low-frequency transform for domain generalization, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 24132\u201324141.","DOI":"10.1109\/CVPR52729.2023.02311"},{"key":"10.1016\/j.patcog.2026.113826_b13","article-title":"Frequency-spatial complementation: Unified channel-specific style attack for cross-domain few-shot learning","author":"Ji","year":"2025","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.113826_b14","series-title":"Cross-domain few-shot classification via adversarial task augmentation","author":"Wang","year":"2021"},{"key":"10.1016\/j.patcog.2026.113826_b15","series-title":"Cross-domain few-shot classification via learned feature-wise transformation","author":"Tseng","year":"2020"},{"key":"10.1016\/j.patcog.2026.113826_b16","doi-asserted-by":"crossref","unstructured":"Y. Fu, Y. Xie, Y. Fu, Y.-G. Jiang, StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 24575\u201324584.","DOI":"10.1109\/CVPR52729.2023.02354"},{"key":"10.1016\/j.patcog.2026.113826_b17","doi-asserted-by":"crossref","unstructured":"P. Li, S. Gong, C. Wang, Y. Fu, Ranking distance calibration for cross-domain few-shot learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 9099\u20139108.","DOI":"10.1109\/CVPR52688.2022.00889"},{"key":"10.1016\/j.patcog.2026.113826_b18","series-title":"Cross-domain few-shot learning with unlabelled data","author":"Yao","year":"2021"},{"key":"10.1016\/j.patcog.2026.113826_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110358","article-title":"Self-taught cross-domain few-shot learning with weakly supervised object localization and task-decomposition","volume":"265","author":"Liu","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.patcog.2026.113826_b20","doi-asserted-by":"crossref","first-page":"116783","DOI":"10.52202\/079017-3707","article-title":"Meta-exploiting frequency prior for cross-domain few-shot learning","volume":"37","author":"Zhou","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113826_b21","series-title":"Exploring cross-domain few-shot classification via frequency-aware prompting","author":"Zhang","year":"2024"},{"issue":"10","key":"10.1016\/j.patcog.2026.113826_b22","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.1109\/TPAMI.2018.2857824","article-title":"Packing convolutional neural networks in the frequency domain","volume":"41","author":"Wang","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113826_b23","series-title":"Masked frequency modeling for self-supervised visual pre-training","author":"Xie","year":"2022"},{"key":"10.1016\/j.patcog.2026.113826_b24","doi-asserted-by":"crossref","unstructured":"J. Huang, D. Guan, A. Xiao, S. Lu, Fsdr: Frequency space domain randomization for domain generalization, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 6891\u20136902.","DOI":"10.1109\/CVPR46437.2021.00682"},{"key":"10.1016\/j.patcog.2026.113826_b25","unstructured":"J. Huang, D. Guan, A. Xiao, S.L. FSDR, Frequency space domain randomization for domain generalization, in: Proc. of CVPR, pp. 6891\u20136902."},{"key":"10.1016\/j.patcog.2026.113826_b26","unstructured":"R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, in: International Conference on Learning Representations, 2018."},{"key":"10.1016\/j.patcog.2026.113826_b27","series-title":"International Conference on Machine Learning","first-page":"1745","article-title":"Band-limited training and inference for convolutional neural networks","author":"Dziedzic","year":"2019"},{"key":"10.1016\/j.patcog.2026.113826_b28","series-title":"GLOBECOM 2022-2022 IEEE Global Communications Conference","first-page":"480","article-title":"Few-shot SAR target classification combining both spatial and frequency information","author":"Li","year":"2022"},{"key":"10.1016\/j.patcog.2026.113826_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113389","article-title":"FSPDF: Few-shot learning with progressive dual-domain feature fusion via self-supervised learning","volume":"317","author":"Li","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.patcog.2026.113826_b30","doi-asserted-by":"crossref","unstructured":"Y. Luo, Y. Zhang, J. Yan, W. Liu, Generalizing face forgery detection with high-frequency features, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 16317\u201316326.","DOI":"10.1109\/CVPR46437.2021.01605"},{"key":"10.1016\/j.patcog.2026.113826_b31","series-title":"European Conference on Computer Vision","first-page":"1","article-title":"Improving vision transformers by revisiting high-frequency components","author":"Bai","year":"2022"},{"key":"10.1016\/j.patcog.2026.113826_b32","doi-asserted-by":"crossref","first-page":"10264","DOI":"10.1109\/TMM.2024.3405713","article-title":"Few-shot fine-grained image classification via multi-frequency neighborhood and double-cross modulation","volume":"26","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.patcog.2026.113826_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106536","article-title":"Spectral decomposition and transformation for cross-domain few-shot learning","volume":"179","author":"Liu","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.patcog.2026.113826_b34","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.patcog.2026.113826_b35","series-title":"Computer Vision\u2013ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV 11","first-page":"438","article-title":"Visual recognition with humans in the loop","author":"Branson","year":"2010"},{"key":"10.1016\/j.patcog.2026.113826_b36","doi-asserted-by":"crossref","unstructured":"J. Krause, M. Stark, J. Deng, L. Fei-Fei, 3d object representations for fine-grained categorization, in: Proceedings of the IEEE International Conference on Computer Vision Workshops, 2013, pp. 554\u2013561.","DOI":"10.1109\/ICCVW.2013.77"},{"issue":"6","key":"10.1016\/j.patcog.2026.113826_b37","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","article-title":"Places: A 10 million image database for scene recognition","volume":"40","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113826_b38","doi-asserted-by":"crossref","unstructured":"G. Van Horn, O. Mac Aodha, Y. Song, Y. Cui, C. Sun, A. Shepard, H. Adam, P. Perona, S. Belongie, The inaturalist species classification and detection dataset, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8769\u20138778.","DOI":"10.1109\/CVPR.2018.00914"},{"key":"10.1016\/j.patcog.2026.113826_b39","series-title":"2020 25th International Conference on Pattern Recognition","first-page":"7609","article-title":"Explanation-guided training for cross-domain few-shot classification","author":"Sun","year":"2021"},{"key":"10.1016\/j.patcog.2026.113826_b40","series-title":"European Conference on Computer Vision","first-page":"20","article-title":"Adversarial feature augmentation for cross-domain few-shot classification","author":"Hu","year":"2022"},{"key":"10.1016\/j.patcog.2026.113826_b41","series-title":"Flatten long-range loss landscapes for cross-domain few-shot learning","author":"Zou","year":"2024"},{"issue":"10","key":"10.1016\/j.patcog.2026.113826_b42","article-title":"Adaptive prototype learning algorithms: Theoretical and experimental studies.","volume":"7","author":"Chang","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.patcog.2026.113826_b43","first-page":"1","article-title":"Cross-domain few-shot learning based on feature disentanglement for hyperspectral image classification","volume":"62","author":"Qin","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.113826_b44","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326007910?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326007910?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:22:37Z","timestamp":1783167757000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326007910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":44,"alternative-id":["S0031320326007910"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113826","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Frequency-guided generalizable representation learning for cross-domain few-shot learning","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113826","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"113826"}}