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Surv."],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:p>While deep learning excels in computer vision tasks with abundant labeled data, its performance diminishes significantly in scenarios with limited labeled samples. To address this, Few-shot learning (FSL) enables models to perform the target tasks with very few labeled examples by leveraging prior knowledge from related tasks. However, traditional FSL assumes that both the related and target tasks come from the same domain, which is a restrictive assumption in many real-world scenarios where domain differences are common. To overcome this limitation, Cross-domain few-shot learning (CDFSL) has gained attention, as it allows source and target data to come from different domains and label spaces. This article presents the first comprehensive review of Cross-domain Few-shot Learning (CDFSL), a field that has received less attention compared to traditional FSL due to its unique challenges. We aim at providing both a position article and a tutorial for researchers, covering key problems, existing methods, and future research directions. The review begins with a formal definition of CDFSL, outlining its core challenges, followed by a systematic analysis of current approaches, organized under a clear taxonomy. Finally, we discuss promising future directions in terms of problem setups, applications, and theoretical advancements.<\/jats:p>","DOI":"10.1145\/3718362","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T11:27:56Z","timestamp":1739791676000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2335-420X","authenticated-orcid":false,"given":"Huali","family":"Xu","sequence":"first","affiliation":[{"name":"College of Computer Science, Nankai University, Tianjin, China and Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5927-5426","authenticated-orcid":false,"given":"Shuaifeng","family":"Zhi","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8453-3346","authenticated-orcid":false,"given":"Shuzhou","family":"Sun","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5239-692X","authenticated-orcid":false,"given":"Vishal","family":"Patel","sequence":"additional","affiliation":[{"name":"Johns Hopkins University, Baltimore, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2011-2873","authenticated-orcid":false,"given":"Li","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Thomas Adler Johannes Brandstetter Michael Widrich Andreas Mayr David Kreil Michael Kopp G\u00fcnter Klambauer and Sepp Hochreiter. 2020. Cross-domain few-shot learning by representation fusion. arXiv preprint arXiv:2010.06498 (2020)."},{"key":"e_1_3_2_3_2","first-page":"275","volume-title":"Proceedings of the Future Technologies Conference, Volume 1","author":"Akinrinade Olusoji","year":"2022","unstructured":"Olusoji Akinrinade, Chunglin Du, Samuel Ajila, and Toluwase A. Olowookere. 2022. Deep learning and few-shot learning in the detection of skin cancer: An overview. In Proceedings of the Future Technologies Conference, Volume 1. Springer, 275\u2013286."},{"key":"e_1_3_2_4_2","unstructured":"Martin Arjovsky L\u00e9on Bottou Ishaan Gulrajani and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)."},{"key":"e_1_3_2_5_2","first-page":"1","article-title":"Hybrid approaches to optimization and machine learning methods: A systematic literature review","author":"Azevedo Beatriz Flamia","year":"2024","unstructured":"Beatriz Flamia Azevedo, Ana Maria A. C. Rocha, and Ana I. 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Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et\u00a0al. 2020. Language models are few-shot learners. advances in Neural Information Processing Systems 33 (2020), 1877\u20131901.","journal-title":"advances in Neural Information Processing Systems"},{"key":"e_1_3_2_9_2","unstructured":"John Cai Bill Cai and Sheng Mei Shen. 2020. SB-MTL: Score-based meta transfer-learning for cross-domain few-shot learning. arXiv preprint arXiv:2012.01784 (2020)."},{"key":"e_1_3_2_10_2","volume-title":"Multitask Learning","author":"Caruana Rich","year":"1998","unstructured":"Rich Caruana. 1998. Multitask Learning. Springer."},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Qiaoling Chen Zhihao Chen and Wei Luo. 2022. Feature transformation for cross-domain few-shot remote sensing scene classification. In International Conference on Pattern Recognition. 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In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 1835\u20131845."},{"key":"e_1_3_2_18_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Das Debasmit","year":"2022","unstructured":"Debasmit Das, Sungrack Yun, and Fatih Porikli. 2022. ConfeSS: A framework for single source cross-domain few-shot learning. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_19_2","first-page":"3","volume-title":"Proceedings of the International Conference on Big Data","author":"Deng Shisheng","year":"2023","unstructured":"Shisheng Deng, Dongping Liao, Xitong Gao, Juanjuan Zhao, and Kejiang Ye. 2023. A survey on cross-domain few-shot image classification. In Proceedings of the International Conference on Big Data. Springer, 3\u201317."},{"key":"e_1_3_2_20_2","first-page":"754","volume-title":"Proceedings of the International Symposium on Computer and Information Processing Technology","author":"Ding Yuan","year":"2021","unstructured":"Yuan Ding and Ping Wang. 2021. Reasearch on cross domain few-shot learning method based on local feature association. In Proceedings of the International Symposium on Computer and Information Processing Technology. IEEE, 754\u2013759."},{"key":"e_1_3_2_21_2","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer G. Heigold S. Gelly and others. 2020. An image is worth \\(16\\times 16\\) words: Transformers for image recognition at scale. In International Conference on Learning Representations."},{"key":"e_1_3_2_22_2","unstructured":"Yingjun Du Xiantong Zhen Ling Shao and Cees GM Snoek. 2022. Hierarchical variational memory for few-shot learning across domains. In 10th International Conference on Learning Representations (ICLR\u201922)."},{"key":"e_1_3_2_23_2","first-page":"769","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Dvornik Nikita","year":"2020","unstructured":"Nikita Dvornik, Cordelia Schmid, and Julien Mairal. 2020. Selecting relevant features from a multi-domain representation for few-shot classification. In Proceedings of the European Conference on Computer Vision. Springer, 769\u2013786."},{"key":"e_1_3_2_24_2","first-page":"1","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"Edwards Harrison","year":"2017","unstructured":"Harrison Edwards and Amos Storkey. 2017. Towards a neural statistician. 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In Proceedings of the 29th ACM International Conference on Multimedia. 5326\u20135334."},{"key":"e_1_3_2_32_2","first-page":"6609","volume-title":"Proceedings of the 30th ACM International Conference on Multimedia","author":"Fu Yuqian","year":"2022","unstructured":"Yuqian Fu, Yu Xie, Yanwei Fu, Jingjing Chen, and Yu-Gang Jiang. 2022. ME-D2N: Multi-expert domain decompositional network for cross-domain few-shot learning. In Proceedings of the 30th ACM International Conference on Multimedia. 6609\u20136617."},{"key":"e_1_3_2_33_2","unstructured":"Yuqian Fu Yu Xie Yanwei Fu Jingjing Chen and Yu-Gang Jiang. 2022. Wave-SAN: Wavelet based style augmentation network for cross-domain few-shot learning. arXiv preprint arXiv:2203.07656 (2022)."},{"key":"e_1_3_2_34_2","first-page":"24575","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Fu Yuqian","year":"2023","unstructured":"Yuqian Fu, Yu Xie, Yanwei Fu, and Yu-Gang Jiang. 2023. Styleadv: Meta style adversarial training for cross-domain few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 24575\u201324584."},{"key":"e_1_3_2_35_2","first-page":"3816","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Gao Tianyu","year":"2021","unstructured":"Tianyu Gao, Adam Fisch, and Danqi Chen. 2021. Making pre-trained language models better few-shot learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 3816\u20133830."},{"key":"e_1_3_2_36_2","first-page":"3816","volume-title":"Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021","author":"Gao Tianyu","year":"2021","unstructured":"Tianyu Gao, Adam Fisch, and Danqi Chen. 2021. Making pre-trained language models better few-shot learners. In Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021. Association for Computational Linguistics (ACL), 3816\u20133830."},{"key":"e_1_3_2_37_2","unstructured":"Yunhe Gao Xingjian Shi Yi Zhu Hao Wang Zhiqiang Tang Xiong Zhou Mu Li and Dimitris N. Metaxas. 2022. Visual prompt tuning for test-time domain adaptation. arXiv preprint arXiv:2210.04831 (2022)."},{"key":"e_1_3_2_38_2","first-page":"673","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Gao Yipeng","year":"2022","unstructured":"Yipeng Gao, Lingxiao Yang, Yunmu Huang, Song Xie, Shiyong Li, and Wei-Shi Zheng. 2022. AcroFOD: An adaptive method for cross-domain few-shot object detection. In Proceedings of the European Conference on Computer Vision. Springer, 673\u2013690."},{"key":"e_1_3_2_39_2","unstructured":"Victor Garcia and Joan Bruna. 2018. Few-shot learning with graph neural networks. In 6th International Conference on Learning Representations (ICLR\u201918)."},{"key":"e_1_3_2_40_2","first-page":"5721","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Gondal Muhammad Waleed","year":"2024","unstructured":"Muhammad Waleed Gondal, Jochen Gast, Inigo Alonso Ruiz, Richard Droste, Tommaso Macri, Suren Kumar, and Luitpold Staudigl. 2024. Domain aligned CLIP for few-shot classification. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 5721\u20135730."},{"issue":"1","key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1038\/s41598-023-28588-y","article-title":"Cross-domain few-shot learning based on pseudo-Siamese neural network","volume":"13","author":"Gong Yuxuan","year":"2023","unstructured":"Yuxuan Gong, Yuqi Yue, Weidong Ji, and Guohui Zhou. 2023. Cross-domain few-shot learning based on pseudo-Siamese neural network. Scientific Reports 13, 1 (2023), 1427.","journal-title":"Scientific Reports"},{"key":"e_1_3_2_42_2","volume-title":"Deep Learning","author":"Goodfellow Ian","year":"2016","unstructured":"Ian Goodfellow. 2016. Deep Learning. MIT Press."},{"issue":"6","key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: A survey","volume":"129","author":"Gou Jianping","year":"2021","unstructured":"Jianping Gou, Baosheng Yu, Stephen J. Maybank, and Dacheng Tao. 2021. Knowledge distillation: A survey. International Journal of Computer Vision 129, 6 (2021), 1789\u20131819.","journal-title":"International Journal of Computer Vision"},{"key":"e_1_3_2_44_2","volume-title":"Proceedings of the Asian Conference on Computer Vision","author":"Guan Jiechao","year":"2020","unstructured":"Jiechao Guan, Manli Zhang, and Zhiwu Lu. 2020. Large-scale cross-domain few-shot learning. In Proceedings of the Asian Conference on Computer Vision."},{"key":"e_1_3_2_45_2","first-page":"124","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Guo Yunhui","year":"2020","unstructured":"Yunhui Guo, Noel C. Codella, Leonid Karlinsky, James V. Codella, John R. Smith, Kate Saenko, Tajana Rosing, and Rogerio Feris. 2020. A broader study of cross-domain few-shot learning. In Proceedings of the European Conference on Computer Vision. Springer, 124\u2013141."},{"key":"e_1_3_2_46_2","first-page":"1590","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Guo Yurong","year":"2023","unstructured":"Yurong Guo, Ruoyi Du, Yuan Dong, Timothy Hospedales, Yi-Zhe Song, and Zhanyu Ma. 2023. Task-aware adaptive learning for cross-domain few-shot learning. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 1590\u20131599."},{"key":"e_1_3_2_47_2","doi-asserted-by":"crossref","unstructured":"Kaveh Hassani. 2022. Cross-domain few-shot graph classification. In Proceedings of the AAAI Conference on Artificial Intelligence 36 6 (2022) 6856\u20136864.","DOI":"10.1609\/aaai.v36i6.20642"},{"key":"e_1_3_2_48_2","first-page":"770","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770\u2013778."},{"issue":"7","key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","article-title":"Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification","volume":"12","author":"Helber Patrick","year":"2019","unstructured":"Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. 2019. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, 7 (2019), 2217\u20132226. Retrieved from https:\/\/github.com\/phelber\/eurosat","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"e_1_3_2_50_2","unstructured":"Geoffrey Hinton Oriol Vinyals and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"issue":"9","key":"e_1_3_2_51_2","first-page":"5149","article-title":"Meta-learning in neural networks: A survey","volume":"44","author":"Hospedales Timothy","year":"2021","unstructured":"Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. 2021. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 9 (2021), 5149\u20135169.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_52_2","first-page":"1","volume-title":"Proceedings of the International Joint Conference on Neural Networks","author":"Houben Sebastian","year":"2013","unstructured":"Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel. 2013. Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1\u20138. Retrieved 09 January 2014 from https:\/\/www.kaggle.com\/datasets\/meowmeowmeowmeowmeow\/gtsrb-german-traffic-sign"},{"key":"e_1_3_2_53_2","first-page":"2790","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Houlsby Neil","year":"2019","unstructured":"Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In Proceedings of the International Conference on Machine Learning. PMLR, 2790\u20132799."},{"key":"e_1_3_2_54_2","first-page":"20","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Hu Yanxu","year":"2022","unstructured":"Yanxu Hu and Andy J. Ma. 2022. Adversarial feature augmentation for cross-domain few-shot classification. In Proceedings of the European Conference on Computer Vision. Springer, 20\u201337."},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"108304","DOI":"10.1016\/j.patcog.2021.108304","article-title":"Unsupervised descriptor selection based meta-learning networks for few-shot classification","volume":"122","author":"Hu Zhengping","year":"2022","unstructured":"Zhengping Hu, Zijun Li, Xueyu Wang, and Saiyue Zheng. 2022. Unsupervised descriptor selection based meta-learning networks for few-shot classification. Pattern Recognition 122 (2022), 108304.","journal-title":"Pattern Recognition"},{"key":"e_1_3_2_56_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hu Zhengdong","year":"2022","unstructured":"Zhengdong Hu, Yifan Sun, and Yi Yang. 2022. Switch to generalize: Domain-switch learning for cross-domain few-shot classification. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_57_2","first-page":"3584","article-title":"Dynamic distillation network for cross-domain few-shot recognition with unlabeled data","volume":"34","author":"Islam Ashraful","year":"2021","unstructured":"Ashraful Islam, Chun-Fu Richard Chen, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, and Richard J. Radke. 2021. Dynamic distillation network for cross-domain few-shot recognition with unlabeled data. advances in Neural Information Processing Systems 34 (2021), 3584\u20133595.","journal-title":"advances in Neural Information Processing Systems"},{"key":"e_1_3_2_58_2","article-title":"Cross-domain few-shot classification via dense-sparse-dense regularization","author":"Ji Fanfan","year":"2023","unstructured":"Fanfan Ji, Yunpeng Chen, Luoqi Liu, and Xiao-Tong Yuan. 2023. Cross-domain few-shot classification via dense-sparse-dense regularization. IEEE Transactions on Circuits and Systems for Video Technology 34, 3 (2023), 1352\u20131363.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_2_59_2","article-title":"Soft weight pruning for cross-domain few-shot learning with unlabeled target data","author":"Ji Fanfan","year":"2024","unstructured":"Fanfan Ji, Xiao-Tong Yuan, and Qingshan Liu. 2024. Soft weight pruning for cross-domain few-shot learning with unlabeled target data. IEEE Transactions on Multimedia 26 (2024), 6759\u20136769.","journal-title":"IEEE Transactions on Multimedia"},{"issue":"2","key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s13735-024-00333-9","article-title":"Relevance equilibrium network for cross-domain few-shot learning","volume":"13","author":"Ji Zhong","year":"2024","unstructured":"Zhong Ji, Xiangyu Kong, Xuan Wang, and Xiyao Liu. 2024. Relevance equilibrium network for cross-domain few-shot learning. International Journal of Multimedia Information Retrieval 13, 2 (2024), 21.","journal-title":"International Journal of Multimedia Information Retrieval"},{"issue":"2","key":"e_1_3_2_61_2","doi-asserted-by":"crossref","first-page":"172312","DOI":"10.1007\/s11704-022-1250-2","article-title":"Teachers cooperation: Team-knowledge distillation for multiple cross-domain few-shot learning","volume":"17","author":"Ji Zhong","year":"2023","unstructured":"Zhong Ji, Jingwei Ni, Xiyao Liu, and Yanwei Pang. 2023. Teachers cooperation: Team-knowledge distillation for multiple cross-domain few-shot learning. Frontiers of Computer Science 17, 2 (2023), 172312.","journal-title":"Frontiers of Computer Science"},{"key":"e_1_3_2_62_2","unstructured":"Jonas Jongejan Henry Rowley Takashi Kawashima Jongmin Kim and Nick Fox-Gieg. 2016. The quick draw!-ai experiment. Retrieved February 17 2018 from https:\/\/github.com\/googlecreativelab\/quickdraw-dataset"},{"issue":"9","key":"e_1_3_2_63_2","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.3390\/sym11091066","article-title":"Deep metric learning: A survey","volume":"11","author":"Kaya Mahmut","year":"2019","unstructured":"Mahmut Kaya and Hasan \u015eakir Bilge. 2019. Deep metric learning: A survey. Symmetry 11, 9 (2019), 1066.","journal-title":"Symmetry"},{"key":"e_1_3_2_64_2","first-page":"22","volume-title":"Aea Papers and Proceedings","volume":"108","author":"Kleinberg Jon","year":"2018","unstructured":"Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ashesh Rambachan. 2018. Algorithmic fairness. In Aea Papers and Proceedings, Vol. 108. 22\u201327."},{"key":"e_1_3_2_65_2","first-page":"554","volume-title":"Proceedings of the IEEE International Conference on Computer Vision Workshops","author":"Krause Jonathan","year":"2013","unstructured":"Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 2013. 3d object representations for fine-grained categorization. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 554\u2013561. Retrieved from http:\/\/ai.stanford.edu\/~jkrause\/cars\/car_dataset.html"},{"issue":"6","key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky Alex","year":"2017","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM 60, 6 (2017), 84\u201390.","journal-title":"Communications of the ACM"},{"key":"e_1_3_2_67_2","volume-title":"Proceedings of the Annual Meeting of the Cognitive Science Society","author":"Lake Brenden","year":"2011","unstructured":"Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum. 2011. One shot learning of simple visual concepts. In Proceedings of the Annual Meeting of the Cognitive Science Society. Retrieved from https:\/\/github.com\/brendenlake\/omniglot"},{"issue":"6266","key":"e_1_3_2_68_2","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1126\/science.aab3050","article-title":"Human-level concept learning through probabilistic program induction","volume":"350","author":"Lake Brenden M.","year":"2015","unstructured":"Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum. 2015. Human-level concept learning through probabilistic program induction. Science 350, 6266 (2015), 1332\u20131338.","journal-title":"Science"},{"issue":"7553","key":"e_1_3_2_69_2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436\u2013444.","journal-title":"Nature"},{"key":"e_1_3_2_70_2","first-page":"1715","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing","author":"Lee Wei-Yu","year":"2022","unstructured":"Wei-Yu Lee, Jheng-Yu Wang, and Yu-Chiang Frank Wang. 2022. Domain-agnostic meta-learning for cross-domain few-shot classification. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 1715\u20131719."},{"key":"e_1_3_2_71_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Li Da","year":"2018","unstructured":"Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. 2018. Learning to generalize: Meta-learning for domain generalization. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_72_2","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.neucom.2021.01.123","article-title":"Multi-domain few-shot image recognition with knowledge transfer","volume":"442","author":"Li Mingxi","year":"2021","unstructured":"Mingxi Li, Ronggui Wang, Juan Yang, Lixia Xue, and Min Hu. 2021. Multi-domain few-shot image recognition with knowledge transfer. Neurocomputing 442 (2021), 64\u201372.","journal-title":"Neurocomputing"},{"key":"e_1_3_2_73_2","first-page":"9099","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Li Pan","year":"2022","unstructured":"Pan Li, Shaogang Gong, Chengjie Wang, and Yanwei Fu. 2022. Ranking distance calibration for cross-domain few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 9099\u20139108."},{"key":"e_1_3_2_74_2","article-title":"Task context transformer and Gcn for few-shot learning of cross-domain","author":"Li Pengfang","year":"2023","unstructured":"Pengfang Li, Fang Liu, Licheng Jiao, Lingling Li, Puhua Chen, and Shuo Li. 2023. Task context transformer and Gcn for few-shot learning of cross-domain. Available at SSRN 4342068 (2023).","journal-title":"Available at SSRN 4342068"},{"key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"109652","DOI":"10.1016\/j.patcog.2023.109652","article-title":"Knowledge transduction for cross-domain few-shot learning","volume":"141","author":"Li Pengfang","year":"2023","unstructured":"Pengfang Li, Fang Liu, Licheng Jiao, Shuo Li, Lingling Li, Xu Liu, and Xinyan Huang. 2023. Knowledge transduction for cross-domain few-shot learning. Pattern Recognition 141 (2023), 109652.","journal-title":"Pattern Recognition"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2025.3531988"},{"key":"e_1_3_2_77_2","first-page":"326","article-title":"Predicting gradient is better: Exploring self-supervised learning for sar atr with a joint-embedding predictive architecture","volume":"218","author":"Li Weijie","year":"2024","unstructured":"Weijie Li, Wei Yang, Tianpeng Liu, Yuenan Hou, Yuxuan Li, Zhen Liu, Yongxiang Liu, and Li Liu. 2024. Predicting gradient is better: Exploring self-supervised learning for sar atr with a joint-embedding predictive architecture. ISPRS JPRS 218 (2024), 326\u2013338.","journal-title":"ISPRS JPRS"},{"key":"e_1_3_2_78_2","first-page":"9526","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Li Wei-Hong","year":"2021","unstructured":"Wei-Hong Li, Xialei Liu, and Hakan Bilen. 2021. Universal representation learning from multiple domains for few-shot classification. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 9526\u20139535."},{"key":"e_1_3_2_79_2","first-page":"7161","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Li Wei-Hong","year":"2022","unstructured":"Wei-Hong Li, Xialei Liu, and Hakan Bilen. 2022. Cross-domain few-shot learning with task-specific adapters. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 7161\u20137170."},{"key":"e_1_3_2_80_2","doi-asserted-by":"crossref","unstructured":"Wei-Hong Li Xialei Liu and Hakan Bilen. 2024. Universal representations: A unified look at multiple task and domain learning. International Journal of Computer Vision 132 5 (2024) 1521\u20131545.","DOI":"10.1007\/s11263-023-01931-6"},{"key":"e_1_3_2_81_2","first-page":"9424","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Liang Hanwen","year":"2021","unstructured":"Hanwen Liang, Qiong Zhang, Peng Dai, and Juwei Lu. 2021. Boosting the generalization capability in cross-domain few-shot learning via noise-enhanced supervised autoencoder. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 9424\u20139434."},{"key":"e_1_3_2_82_2","first-page":"6028","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Liang Jian","year":"2020","unstructured":"Jian Liang, Dapeng Hu, and Jiashi Feng. 2020. Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation. In Proceedings of the International Conference on Machine Learning. PMLR, 6028\u20136039."},{"key":"e_1_3_2_83_2","first-page":"2117","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Lin Tsung-Yi","year":"2017","unstructured":"Tsung-Yi Lin, Piotr Doll\u00e1r, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. 2017. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2117\u20132125."},{"key":"e_1_3_2_84_2","first-page":"740","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Lin Tsung-Yi","year":"2014","unstructured":"Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C. Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Proceedings of the European Conference on Computer Vision. Springer, 740\u2013755. Retrieved from https:\/\/cocodataset.org\/#download"},{"key":"e_1_3_2_85_2","unstructured":"Xiao Lin Meng Ye Yunye Gong Giedrius T. Burachas Ajay Divakaran Yi Yao and Nikoletta Basiou. 2022. Modular adaptation for cross-domain few-shot learning. Google Patents."},{"key":"e_1_3_2_86_2","first-page":"1","article-title":"TScatNet: An interpretable cross-domain intelligent diagnosis model with antinoise and few-shot learning capability","volume":"70","author":"Liu Chao","year":"2020","unstructured":"Chao Liu, Chengjin Qin, Xi Shi, Zengwei Wang, Gang Zhang, and Yunting Han. 2020. TScatNet: An interpretable cross-domain intelligent diagnosis model with antinoise and few-shot learning capability. IEEE Transactions on Instrumentation and Measurement 70, 12 (2020), 1\u201310.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"e_1_3_2_87_2","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep learning for generic object detection: A survey","volume":"128","author":"Liu Li","year":"2020","unstructured":"Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietik\u00e4inen. 2020. Deep learning for generic object detection: A survey. International Journal of Computer Vision 128 (2020), 261\u2013318.","journal-title":"International Journal of Computer Vision"},{"issue":"9","key":"e_1_3_2_88_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3560815","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu Pengfei","year":"2023","unstructured":"Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Computing Surveys 55, 9, 2 (2023), 1\u201335.","journal-title":"Computing Surveys"},{"issue":"11","key":"e_1_3_2_89_2","doi-asserted-by":"crossref","first-page":"12422","DOI":"10.1007\/s10489-021-03124-5","article-title":"Geometric algebra graph neural network for cross-domain few-shot classification","volume":"52","author":"Liu Qifan","year":"2022","unstructured":"Qifan Liu and Wenming Cao. 2022. Geometric algebra graph neural network for cross-domain few-shot classification. Applied Intelligence 52, 11 (2022), 12422\u201312435.","journal-title":"Applied Intelligence"},{"key":"e_1_3_2_90_2","first-page":"110358","article-title":"Self-taught cross-domain few-shot learning with weakly supervised object localization and task-decomposition","author":"Liu Xiyao","year":"2023","unstructured":"Xiyao Liu, Zhong Ji, Yanwei Pang, and Zhi Han. 2023. Self-taught cross-domain few-shot learning with weakly supervised object localization and task-decomposition. Knowledge-Based Systems 265, Article number: 110358 (2023), 110358.","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"e_1_3_2_91_2","article-title":"Deep unsupervised domain adaptation: A review of recent advances and perspectives","volume":"11","author":"Liu Xiaofeng","year":"2022","unstructured":"Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, Hyejin Oh, Georges El Fakhri, Je-Won Kang, and Jonghye Woo. 2022. Deep unsupervised domain adaptation: A review of recent advances and perspectives. APSIPA Transactions on Signal and Information Processing 11, 1 (2022).","journal-title":"APSIPA Transactions on Signal and Information Processing"},{"key":"e_1_3_2_92_2","first-page":"8453","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Liu Yanbin","year":"2021","unstructured":"Yanbin Liu, Juho Lee, Linchao Zhu, Ling Chen, Humphrey Shi, and Yi Yang. 2021. A multi-mode modulator for multi-domain few-shot classification. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 8453\u20138462."},{"key":"e_1_3_2_93_2","doi-asserted-by":"crossref","first-page":"106536","DOI":"10.1016\/j.neunet.2024.106536","article-title":"Spectral decomposition and transformation for cross-domain few-shot learning","volume":"179","author":"Liu Yicong","year":"2024","unstructured":"Yicong Liu, Yixiong Zou, Ruixuan Li, and Yuhua Li. 2024. Spectral decomposition and transformation for cross-domain few-shot learning. Neural Networks 179 (2024), 106536.","journal-title":"Neural Networks"},{"key":"e_1_3_2_94_2","doi-asserted-by":"crossref","unstructured":"Jiang Lu Pinghua Gong Jieping Ye Jianwei Zhang and Changshui Zhang. 2023. A survey on machine learning from few samples. Pattern Recognition 139 (2023) 109480.","DOI":"10.1016\/j.patcog.2023.109480"},{"key":"e_1_3_2_95_2","first-page":"19754","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Ma Tianyi","year":"2023","unstructured":"Tianyi Ma, Yifan Sun, Zongxin Yang, and Yi Yang. 2023. Prod: Prompting-to-disentangle domain knowledge for cross-domain few-shot image classification. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 19754\u201319763."},{"key":"e_1_3_2_96_2","first-page":"8968","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Ma Xinhong","year":"2021","unstructured":"Xinhong Ma, Junyu Gao, and Changsheng Xu. 2021. Active universal domain adaptation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 8968\u20138977."},{"key":"e_1_3_2_97_2","unstructured":"Subhransu Maji Esa Rahtu Juho Kannala Matthew Blaschko and Andrea Vedaldi. 2013. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151 (2013)."},{"key":"e_1_3_2_98_2","article-title":"Domain adaptation with multiple sources","volume":"21","author":"Mansour Yishay","year":"2008","unstructured":"Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. 2008. Domain adaptation with multiple sources. Advances in Neural Information Processing Systems 21 (2008), 1041\u20131048.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"e_1_3_2_99_2","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1146\/annurev.cs.04.060190.002221","article-title":"Machine learning","volume":"4","author":"Mitchell Tom","year":"1990","unstructured":"Tom Mitchell, Bruce Buchanan, Gerald DeJong, Thomas Dietterich, Paul Rosenbloom, and Alex Waibel. 1990. Machine learning. Annual Review of Computer Science 4, 1 (1990), 417\u2013433.","journal-title":"Annual Review of Computer Science"},{"key":"e_1_3_2_100_2","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty Sharada P.","year":"2016","unstructured":"Sharada P. Mohanty, David P. Hughes, and Marcel Salath\u00e9. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7 (2016), 1419. Retrieved from https:\/\/www.kaggle.com\/datasets\/vipoooool\/new-plant-diseases-dataset","journal-title":"Frontiers in Plant Science"},{"key":"e_1_3_2_101_2","volume-title":"Foundations of Machine Learning","author":"Mohri Mehryar","year":"2018","unstructured":"Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2018. Foundations of Machine Learning. MIT Press."},{"key":"e_1_3_2_102_2","article-title":"Few-shot adversarial domain adaptation","volume":"30","author":"Motiian Saeid","year":"2017","unstructured":"Saeid Motiian, Quinn Jones, Seyed Iranmanesh, and Gianfranco Doretto. 2017. Few-shot adversarial domain adaptation. advances in Neural Information Processing Systems 30 (2017), 6670\u20136680.","journal-title":"advances in Neural Information Processing Systems"},{"key":"e_1_3_2_103_2","doi-asserted-by":"crossref","unstructured":"Thomas M\u00fcller Guillermo P\u00e9rez-Torr\u00f3 Angelo Basile and Marc Franco-Salvador. 2022. Active few-shot learning with fasl. In International Conference on Applications of Natural Language to Information Systems. Springer 98\u2013110.","DOI":"10.1007\/978-3-031-08473-7_9"},{"key":"e_1_3_2_104_2","unstructured":"Akihiro Nakamura and Tatsuya Harada. 2019. Revisiting fine-tuning for few-shot learning. arXiv preprint arXiv:1910.00216 (2019)."},{"key":"e_1_3_2_105_2","first-page":"78","volume-title":"Proceedings of the Twenty-first International Conference on Machine Learning","author":"Ng Andrew Y.","year":"2004","unstructured":"Andrew Y. Ng. 2004. Feature selection, L 1 vs. L 2 regularization, and rotational invariance. In Proceedings of the Twenty-first International Conference on Machine Learning. 78."},{"key":"e_1_3_2_106_2","first-page":"722","volume-title":"Proceedings of the 2008 6th Indian Conference on Computer Vision, Graphics and Image Processing","author":"Nilsback Maria-Elena","year":"2008","unstructured":"Maria-Elena Nilsback and Andrew Zisserman. 2008. Automated flower classification over a large number of classes. In Proceedings of the 2008 6th Indian Conference on Computer Vision, Graphics and Image Processing. IEEE, 722\u2013729. Retrieved from https:\/\/www.robots.ox.ac.uk\/~vgg\/data\/flowers\/102\/index.html"},{"key":"e_1_3_2_107_2","doi-asserted-by":"crossref","unstructured":"Jaehoon Oh Sungnyun Kim Namgyu Ho Jin-Hwa Kim Hwanjun Song and Se-Young Yun. 2022. ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4359\u20134363.","DOI":"10.1145\/3511808.3557681"},{"key":"e_1_3_2_108_2","doi-asserted-by":"crossref","first-page":"111458","DOI":"10.1016\/j.knosys.2024.111458","article-title":"Cross-domain few-shot learning via adaptive transformer networks","volume":"288","author":"Paeedeh Naeem","year":"2024","unstructured":"Naeem Paeedeh, Mahardhika Pratama, Muhammad Anwar Ma\u2019sum, Wolfgang Mayer, Zehong Cao, and Ryszard Kowlczyk. 2024. Cross-domain few-shot learning via adaptive transformer networks. Knowledge-Based Systems 288 (2024), 111458.","journal-title":"Knowledge-Based Systems"},{"issue":"10","key":"e_1_3_2_109_2","first-page":"1345","article-title":"A survey on transfer learning","volume":"22","author":"Pan Sinno Jialin","year":"2009","unstructured":"Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (2009), 1345\u20131359.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_110_2","unstructured":"Archit Parnami and Minwoo Lee. 2022. Learning from few examples: A summary of approaches to few-shot learning. arXiv preprint arXiv:2203.04291 (2022)."},{"key":"e_1_3_2_111_2","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et\u00a0al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32 (2019), 8026\u20138037.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"3","key":"e_1_3_2_112_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2014.2347059","article-title":"Visual domain adaptation: A survey of recent advances","volume":"32","author":"Patel Vishal M.","year":"2015","unstructured":"Vishal M. Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa. 2015. Visual domain adaptation: A survey of recent advances. IEEE Signal Processing Magazine 32, 3 (2015), 53\u201369.","journal-title":"IEEE Signal Processing Magazine"},{"key":"e_1_3_2_113_2","unstructured":"Shuman Peng Weilian Song and Martin Ester. 2020. Combining domain-specific meta-learners in the parameter space for cross-domain few-shot classification. arXiv preprint arXiv:2011.00179 (2020)."},{"key":"e_1_3_2_114_2","first-page":"1406","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Peng Xingchao","year":"2019","unstructured":"Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. 2019. Moment matching for multi-source domain adaptation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 1406\u20131415."},{"key":"e_1_3_2_115_2","first-page":"23794","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Perera Rashindrie","year":"2024","unstructured":"Rashindrie Perera and Saman Halgamuge. 2024. Discriminative sample-guided and parameter-efficient feature space adaptation for cross-domain few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 23794\u201323804."},{"key":"e_1_3_2_116_2","unstructured":"Cheng Perng Phoo and Bharath Hariharan. 2020. Self-training for few-shot transfer across extreme task differences. In International Conference on Learning Representations."},{"key":"e_1_3_2_117_2","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2022.3191696","article-title":"A review of generalized zero-shot learning methods","author":"Pourpanah Farhad","year":"2022","unstructured":"Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim, Xi-Zhao Wang, and Q. M. Jonathan Wu. 2022. A review of generalized zero-shot learning methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 4 (2022), 4051\u20134070.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_118_2","unstructured":"Jirui Qi Richong Zhang Chune Li and Yongyi Mao. 2022. Cross domain few-shot learning via meta adversarial training. arXiv preprint arXiv:2202.05713 (2022)."},{"key":"e_1_3_2_119_2","first-page":"8748","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et\u00a0al. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning. PMLR, 8748\u20138763."},{"key":"e_1_3_2_120_2","unstructured":"Shuzhen Rao Jun Huang and Zengming Tang. 2023. Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning. arXiv preprint arXiv:2301.07927 (2023)."},{"key":"e_1_3_2_121_2","first-page":"128117","article-title":"RDProtoFusion: Refined discriminative prototype-based multi-task fusion for cross-domain few-shot learning","author":"Rao Shuzhen","year":"2024","unstructured":"Shuzhen Rao, Jun Huang, and Zengming Tang. 2024. RDProtoFusion: Refined discriminative prototype-based multi-task fusion for cross-domain few-shot learning. Neurocomputing 599 (2024), 128117.","journal-title":"Neurocomputing"},{"key":"e_1_3_2_122_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Ravi Sachin","year":"2017","unstructured":"Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_123_2","unstructured":"Mengye Ren Eleni Triantafillou Sachin Ravi Jake Snell Kevin Swersky Joshua B. Tenenbaum Hugo Larochelle and Richard S. Zemel. 2018. Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676 (2018)."},{"issue":"6","key":"e_1_3_2_124_2","doi-asserted-by":"crossref","first-page":"4733","DOI":"10.1007\/s00521-021-06627-x","article-title":"Attentive fine-grained recognition for cross-domain few-shot classification","volume":"34","author":"Sa Liangbing","year":"2022","unstructured":"Liangbing Sa, Chongchong Yu, Xianqin Ma, Xia Zhao, and Tao Xie. 2022. Attentive fine-grained recognition for cross-domain few-shot classification. Neural Computing and Applications 34, 6 (2022), 4733\u20134746.","journal-title":"Neural Computing and Applications"},{"key":"e_1_3_2_125_2","first-page":"16282","article-title":"Universal domain adaptation through self supervision","volume":"33","author":"Saito Kuniaki","year":"2020","unstructured":"Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko. 2020. Universal domain adaptation through self supervision. Advances in Neural Information Processing Systems 33 (2020), 16282\u201316292.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_126_2","first-page":"11643","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Samarasinghe Sarinda","year":"2023","unstructured":"Sarinda Samarasinghe, Mamshad Nayeem Rizve, Navid Kardan, and Mubarak Shah. 2023. CDFSL-V: Cross-domain few-shot learning for videos. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 11643\u201311652."},{"key":"e_1_3_2_127_2","first-page":"1842","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Santoro Adam","year":"2016","unstructured":"Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap. 2016. Meta-learning with memory-augmented neural networks. In Proceedings of the International Conference on Machine Learning. PMLR, 1842\u20131850."},{"key":"e_1_3_2_128_2","article-title":"Fgvcx fungi classification challenge 2018","author":"Schroeder Brigit","year":"2018","unstructured":"Brigit Schroeder and Yin Cui. 2018. Fgvcx fungi classification challenge 2018. Available Online: github. com\/visipedia\/fgvcx_fungi_comp. Retrieved July 14, 2021 from https:\/\/www.kaggle.com\/c\/fungi-challenge-fgvc-2018","journal-title":"Available Online: github. com\/visipedia\/fgvcx_fungi_comp."},{"key":"e_1_3_2_129_2","unstructured":"Jun Shu Zongben Xu and Deyu Meng. 2018. Small sample learning in big data era. arXiv preprint arXiv:1808.04572 (2018)."},{"key":"e_1_3_2_130_2","first-page":"3173","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Shyam Pranav","year":"2017","unstructured":"Pranav Shyam, Shubham Gupta, and Ambedkar Dukkipati. 2017. Attentive recurrent comparators. In Proceedings of the International Conference on Machine Learning. PMLR, 3173\u20133181."},{"key":"e_1_3_2_131_2","article-title":"Prototypical networks for few-shot learning","author":"Snell Jake","year":"2017","unstructured":"Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. Advances in Neural Information Processing Systems 30 (2017), 4077\u20134087.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1145\/3582688"},{"key":"e_1_3_2_133_2","first-page":"4590","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Sreenivas Manogna","year":"2023","unstructured":"Manogna Sreenivas and Soma Biswas. 2023. Similar class style augmentation for efficient cross-domain few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4590\u20134598."},{"issue":"1","key":"e_1_3_2_134_2","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15, 1 (2014), 1929\u20131958.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_135_2","first-page":"739","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Su Jong-Chyi","year":"2020","unstructured":"Jong-Chyi Su, Yi-Hsuan Tsai, Kihyuk Sohn, Buyu Liu, Subhransu Maji, and Manmohan Chandraker. 2020. Active adversarial domain adaptation. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 739\u2013748."},{"key":"e_1_3_2_136_2","first-page":"7609","volume-title":"Proceedings of the International Conference on Pattern Recognition","author":"Sun Jiamei","year":"2021","unstructured":"Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, and Alexander Binder. 2021. Explanation-guided training for cross-domain few-shot classification. In Proceedings of the International Conference on Pattern Recognition. IEEE, 7609\u20137616."},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3288134"},{"key":"e_1_3_2_138_2","first-page":"1199","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Sung Flood","year":"2018","unstructured":"Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales. 2018. Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1199\u20131208."},{"key":"e_1_3_2_139_2","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Tao Xiaoyu","year":"2020","unstructured":"Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong. 2020. Few-shot class-incremental learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_140_2","unstructured":"Eleni Triantafillou Tyler Zhu Vincent Dumoulin Pascal Lamblin Utku Evci Kelvin Xu Ross Goroshin Carles Gelada Kevin Swersky Pierre-Antoine Manzagol and others. 2019. Meta-Dataset: A dataset of datasets for learning to learn from few examples. In International Conference on Learning Representations."},{"key":"e_1_3_2_141_2","first-page":"7852","article-title":"On the theory of transfer learning: The importance of task diversity","volume":"33","author":"Tripuraneni Nilesh","year":"2020","unstructured":"Nilesh Tripuraneni, Michael Jordan, and Chi Jin. 2020. On the theory of transfer learning: The importance of task diversity. Advances in Neural Information Processing Systems 33 (2020), 7852\u20137862.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"e_1_3_2_142_2","first-page":"1","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl Philipp","year":"2018","unstructured":"Philipp Tschandl, Cliff Rosendahl, and Harald Kittler. 2018. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 5, 1 (2018), 1\u20139. Retrieved from https:\/\/challenge.isic-archive.com\/data\/#2018","journal-title":"Scientific Data"},{"key":"e_1_3_2_143_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Tseng Hung-Yu","year":"2020","unstructured":"Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, and Ming-Hsuan Yang. 2020. Cross-domain few-shot classification via learned feature-wise transformation. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_144_2","first-page":"1138","volume-title":"Proceedings of the IEEE International Conference on Big Data","author":"Tu Pei-Cheng","year":"2021","unstructured":"Pei-Cheng Tu and Hsing-Kuo Pao. 2021. A dropout style model augmentation for cross domain few-shot learning. In Proceedings of the IEEE International Conference on Big Data. IEEE, 1138\u20131147."},{"key":"e_1_3_2_145_2","first-page":"8769","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Horn Grant Van","year":"2018","unstructured":"Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. 2018. The inaturalist species classification and detection dataset. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 8769\u20138778. Retrieved from http:\/\/vllab.ucmerced.edu\/ym41608\/projects\/CrossDomainFewShot\/filelists\/plantae.tar.gz"},{"key":"e_1_3_2_146_2","article-title":"Principles of risk minimization for learning theory","volume":"4","author":"Vapnik Vladimir","year":"1991","unstructured":"Vladimir Vapnik. 1991. Principles of risk minimization for learning theory. Advances in Neural Information Processing Systems 4 (1991), 831\u2013838.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_147_2","first-page":"5018","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Venkateswara Hemanth","year":"2017","unstructured":"Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. 2017. Deep hashing network for unsupervised domain adaptation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 5018\u20135027."},{"key":"e_1_3_2_148_2","unstructured":"Oriol Vinyals Charles Blundell Timothy Lillicrap koray kavukcuoglu and Daan Wierstra. 2016. Matching networks for one shot learning. Advances in Neural Information Processing Systems 29 (2016) 3630\u20133638. Retrieved from http:\/\/vllab.ucmerced.edu\/ym41608\/projects\/CrossDomainFewShot\/filelists\/mini_imagenet_full_size.tar.bz2"},{"key":"e_1_3_2_149_2","unstructured":"Catherine Wah Steve Branson Peter Welinder Pietro Perona and Serge Belongie. 2011. The caltech-ucsd birds-200-2011 dataset. (2011). Retrieved from https:\/\/www.vision.caltech.edu\/datasets\/cub_200_2011\/"},{"key":"e_1_3_2_150_2","doi-asserted-by":"crossref","first-page":"107892","DOI":"10.1016\/j.engappai.2024.107892","article-title":"Cross-domain self-supervised few-shot learning via multiple crops with teacher-student network","volume":"132","author":"Wang Guangpeng","year":"2024","unstructured":"Guangpeng Wang, Yongxiong Wang, Jiapeng Zhang, Xiaoming Wang, and Zhiqun Pan. 2024. Cross-domain self-supervised few-shot learning via multiple crops with teacher-student network. Engineering Applications of Artificial Intelligence 132 (2024), 107892.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"e_1_3_2_151_2","doi-asserted-by":"crossref","unstructured":"Haoqing Wang and Zhi-Hong Deng. 2021. Cross-domain few-shot classification via adversarial task augmentation. arXiv preprint arXiv:2104.14385 (2021).","DOI":"10.24963\/ijcai.2021\/149"},{"key":"e_1_3_2_152_2","article-title":"Generalizing to unseen domains: A survey on domain generalization","author":"Wang Jindong","year":"2022","unstructured":"Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip Yu. 2022. Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering 35, 8 (2022), 8052\u20138072.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_153_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: A survey","volume":"312","author":"Wang Mei","year":"2018","unstructured":"Mei Wang and Weihong Deng. 2018. Deep visual domain adaptation: A survey. Neurocomputing 312 (2018), 135\u2013153.","journal-title":"Neurocomputing"},{"key":"e_1_3_2_154_2","article-title":"Meta-prototypical learning for domain-agnostic few-shot recognition","author":"Wang Rui-Qi","year":"2021","unstructured":"Rui-Qi Wang, Xu-Yao Zhang, and Cheng-Lin Liu. 2021. Meta-prototypical learning for domain-agnostic few-shot recognition. IEEE Transactions on Neural Networks and Learning Systems 33, 11 (2021), 6990\u20136996.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_155_2","doi-asserted-by":"crossref","unstructured":"Wenjian Wang Lijuan Duan Yuxi Wang Junsong Fan Zhi Gong and Zhaoxiang Zhang. 2023. A survey of deep visual cross-domain few-shot learning. arXiv preprint arXiv:2303.09253 (2023).","DOI":"10.2139\/ssrn.4412857"},{"key":"e_1_3_2_156_2","article-title":"MMT: Cross domain few-shot learning via meta-memory transfer","author":"Wang Wenjian","year":"2023","unstructured":"Wenjian Wang, Lijuan Duan, Yuxi Wang, Junsong Fan, and Zhaoxiang Zhang. 2023. MMT: Cross domain few-shot learning via meta-memory transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 12 (2023), 15018\u201315035.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_157_2","unstructured":"Wei Wang Haojie Li Zhengming Ding and Zhihui Wang. 2020. Rethink maximum mean discrepancy for domain adaptation. arXiv preprint arXiv:2007.00689 (2020)."},{"key":"e_1_3_2_158_2","first-page":"2097","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Xiaosong","year":"2017","unstructured":"Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers. 2017. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2097\u20132106. Retrieved from https:\/\/nihcc.app.box.com\/v\/ChestXray-NIHCC"},{"issue":"3","key":"e_1_3_2_159_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3386252","article-title":"Generalizing from a few examples: A survey on few-shot learning","volume":"53","author":"Wang Yaqing","year":"2020","unstructured":"Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. 2020. Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys 53, 3 (2020), 1\u201334.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_2_160_2","first-page":"86","volume-title":"Proceedings of the IEEE International Conference on Network Intelligence and Digital Content","author":"Weng Zhewei","year":"2021","unstructured":"Zhewei Weng, Chunyan Feng, Tiankui Zhang, Yutao Zhu, and Zeren Chen. 2021. Representative multi-domain feature selection based cross-domain few-shot classification. In Proceedings of the IEEE International Conference on Network Intelligence and Digital Content. IEEE, 86\u201390."},{"issue":"5","key":"e_1_3_2_161_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3400066","article-title":"A survey of unsupervised deep domain adaptation","volume":"11","author":"Wilson Garrett","year":"2020","unstructured":"Garrett Wilson and Diane J. Cook. 2020. A survey of unsupervised deep domain adaptation. ACM Transactions on Intelligent Systems and Technology 11, 5 (2020), 1\u201346.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_2_162_2","first-page":"16133","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Woo Sanghyun","year":"2023","unstructured":"Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, and Saining Xie. 2023. Convnext v2: Co-designing and scaling convnets with masked autoencoders. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 16133\u201316142."},{"key":"e_1_3_2_163_2","first-page":"6012","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Wu Jiamin","year":"2024","unstructured":"Jiamin Wu, Xin Liu, Xiaotian Yin, Tianzhu Zhang, and Yongdong Zhang. 2024. Task-adaptive prompted transformer for cross-domain few-shot learning. In Proceedings of the AAAI Conference on Artificial Intelligence. 6012\u20136020."},{"key":"e_1_3_2_164_2","first-page":"1","article-title":"HybridPrompt: Domain-aware prompting for cross-domain few-shot learning","author":"Wu Jiamin","year":"2024","unstructured":"Jiamin Wu, Tianzhu Zhang, and Yongdong Zhang. 2024. HybridPrompt: Domain-aware prompting for cross-domain few-shot learning. International Journal of Computer Vision 132, 6 (2024), 1\u201317.","journal-title":"International Journal of Computer Vision"},{"key":"e_1_3_2_165_2","doi-asserted-by":"crossref","unstructured":"Huali Xu Shuaifeng Zhi and Li Liu. 2023. Cross-domain few-shot classification via inter-source stylization. In 2023 IEEE International Conference on Image Processing (ICIP) IEEE 565\u2013569.","DOI":"10.1109\/ICIP49359.2023.10222701"},{"key":"e_1_3_2_166_2","article-title":"Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning","author":"Xu Huali","year":"2024","unstructured":"Huali Xu, Li Liu, Shuaifeng Zhi, Shaojing Fu, Zhuo Su, Ming-Ming Cheng, and Yongxiang Liu. 2024. Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few-shot learning. IEEE Transactions on Image Processing 33, 3 (2024), 2058\u20132073.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_2_167_2","unstructured":"Huali Xu Yongxiang Liu Li Liu Shuaifeng Zhi Shuzhou Sun Tianpeng Liu and MingMing Cheng. 2024. Step-wise distribution alignment guided style prompt tuning for source-free cross-domain few-shot learning. arXiv preprint arXiv:2411.10070 (2024)."},{"key":"e_1_3_2_168_2","doi-asserted-by":"crossref","first-page":"109811","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 Renjie","year":"2023","unstructured":"Renjie Xu, Lei Xing, Baodi Liu, Dapeng Tao, Weijia Cao, and Weifeng Liu. 2023. Cross-domain few-shot classification via class-shared and class-specific dictionaries. Pattern Recognition 144 (2023), 109811.","journal-title":"Pattern Recognition"},{"key":"e_1_3_2_169_2","article-title":"MemREIN: Rein the domain shift for cross-domain few-shot learning","author":"Xu Yi","year":"2022","unstructured":"Yi Xu, Lichen Wang, Yizhou Wang, Can Qin, Yulun Zhang, and Yun Fu. 2022. MemREIN: Rein the domain shift for cross-domain few-shot learning. International Joint Conference on Artificial Intelligence (2022), 3636\u20133642.","journal-title":"International Joint Conference on Artificial Intelligence"},{"key":"e_1_3_2_170_2","first-page":"438","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining","author":"Xu Ziyun","year":"2023","unstructured":"Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang, and Jun Huang. 2023. Making pre-trained language models end-to-end few-shot learners with contrastive prompt tuning. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining. 438\u2013446."},{"key":"e_1_3_2_171_2","first-page":"549","volume-title":"Proceedings of the 2021 IEEE International Conference on Artificial Intelligence and Computer Applications","author":"Yalan Li","year":"2021","unstructured":"Li Yalan and Wu Jijie. 2021. Cross-domain few-shot classification through diversified feature transformation layers. In Proceedings of the 2021 IEEE International Conference on Artificial Intelligence and Computer Applications. IEEE, 549\u2013555."},{"key":"e_1_3_2_172_2","doi-asserted-by":"publisher","DOI":"10.1017\/9781139061773"},{"key":"e_1_3_2_173_2","first-page":"8978","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Yang Shiqi","year":"2021","unstructured":"Shiqi Yang, Yaxing Wang, Joost Van De Weijer, Luis Herranz, and Shangling Jui. 2021. Generalized source-free domain adaptation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 8978\u20138987."},{"key":"e_1_3_2_174_2","unstructured":"Weijie Li Wei Yang Yuenan Hou Li Liu Yongxiang Liu and Xiang Li. 2025. SARATR-X: Towards building a foundation model for SAR target recognition. IEEE Transactions on Image Processing (2025)."},{"key":"e_1_3_2_175_2","first-page":"16370","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Yang Yongjin","year":"2024","unstructured":"Yongjin Yang, Taehyeon Kim, and Se-Young Yun. 2024. Leveraging normalization layer in adapters with progressive learning and adaptive distillation for cross-domain few-shot learning. In Proceedings of the AAAI Conference on Artificial Intelligence. 16370\u201316378."},{"key":"e_1_3_2_176_2","unstructured":"Fupin Yao. 2021. Cross-domain few-shot learning with unlabelled data. arXiv preprint arXiv:2101.07899 (2021)."},{"key":"e_1_3_2_177_2","first-page":"2868","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yazdanpanah Moslem","year":"2022","unstructured":"Moslem Yazdanpanah and Parham Moradi. 2022. Visual domain bridge: A source-free domain adaptation for cross-domain few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2868\u20132877."},{"key":"e_1_3_2_178_2","first-page":"3293","volume-title":"Proceedings of the 29th ACM International Conference on Multimedia","author":"You Fuming","year":"2021","unstructured":"Fuming You, Jingjing Li, Lei Zhu, Zhi Chen, and Zi Huang. 2021. Domain adaptive semantic segmentation without source data. In Proceedings of the 29th ACM International Conference on Multimedia. 3293\u20133302."},{"key":"e_1_3_2_179_2","first-page":"2720","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"You Kaichao","year":"2019","unstructured":"Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan. 2019. Universal domain adaptation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2720\u20132729."},{"key":"e_1_3_2_180_2","doi-asserted-by":"crossref","first-page":"108704","DOI":"10.1016\/j.patcog.2022.108704","article-title":"A novel forget-update module for few-shot domain generalization","volume":"129","author":"Yuan Minglei","year":"2022","unstructured":"Minglei Yuan, Chunhao Cai, Tong Lu, Yirui Wu, Qian Xu, and Shijie Zhou. 2022. A novel forget-update module for few-shot domain generalization. Pattern Recognition 129 (2022), 108704.","journal-title":"Pattern Recognition"},{"key":"e_1_3_2_181_2","first-page":"1","volume-title":"Proceedings of the IEEE International Conference on Multimedia and Expo","author":"Yuan Wang","year":"2021","unstructured":"Wang Yuan, TianXue Ma, Haichuan Song, Yuan Xie, Zhizhong Zhang, and Lizhuang Ma. 2021. Both comparison and induction are indispensable for cross-domain few-shot learning. In Proceedings of the IEEE International Conference on Multimedia and Expo. IEEE, 1\u20136."},{"key":"e_1_3_2_182_2","first-page":"3215","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Yuan Wang","year":"2022","unstructured":"Wang Yuan, Zhizhong Zhang, Cong Wang, Haichuan Song, Yuan Xie, and Lizhuang Ma. 2022. Task-level self-supervision for cross-domain few-shot learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 3215\u20133223."},{"key":"e_1_3_2_183_2","first-page":"12455","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhang Chi","year":"2021","unstructured":"Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan, and Yinghui Xu. 2021. Few-shot incremental learning with continually evolved classifiers. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 12455\u201312464."},{"issue":"8","key":"e_1_3_2_184_2","doi-asserted-by":"crossref","first-page":"4451","DOI":"10.1007\/s00521-023-09318-x","article-title":"Cross-domain few-shot learning based on feature adaptive distillation","volume":"36","author":"Zhang Dingwei","year":"2024","unstructured":"Dingwei Zhang, Hui Yan, Yadang Chen, Dichao Li, and Chuanyan Hao. 2024. Cross-domain few-shot learning based on feature adaptive distillation. Neural Computing and Applications 36, 8 (2024), 4451\u20134465.","journal-title":"Neural Computing and Applications"},{"issue":"6","key":"e_1_3_2_185_2","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.3390\/sym14061097","article-title":"TACDFSL: Task adaptive cross domain few-shot learning","volume":"14","author":"Zhang Qi","year":"2022","unstructured":"Qi Zhang, Yingluo Jiang, and Zhijie Wen. 2022. TACDFSL: Task adaptive cross domain few-shot learning. Symmetry 14, 6 (2022), 1097.","journal-title":"Symmetry"},{"key":"e_1_3_2_186_2","first-page":"4893","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhang Qiannan","year":"2023","unstructured":"Qiannan Zhang, Shichao Pei, Qiang Yang, Chuxu Zhang, Nitesh V Chawla, and Xiangliang Zhang. 2023. Cross-domain few-shot graph classification with a reinforced task coordinator. In Proceedings of the AAAI Conference on Artificial Intelligence. 4893\u20134901."},{"key":"e_1_3_2_187_2","doi-asserted-by":"crossref","unstructured":"Tiange Zhang Qing Cai Feng Gao Lin Qi and Junyu Dong. 2024. Exploring cross-domain few-shot classification via frequency-aware prompting. arXiv preprint arXiv:2406.16422 (2024).","DOI":"10.24963\/ijcai.2024\/607"},{"key":"e_1_3_2_188_2","first-page":"233","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Zhang Yabin","year":"2018","unstructured":"Yabin Zhang, Hui Tang, and Kui Jia. 2018. Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data. In Proceedings of the European Conference on Computer Vision. 233\u2013248."},{"key":"e_1_3_2_189_2","first-page":"1390","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Zhao An","year":"2021","unstructured":"An Zhao, Mingyu Ding, Zhiwu Lu, Tao Xiang, Yulei Niu, Jiechao Guan, and Ji-Rong Wen. 2021. Domain-adaptive few-shot learning. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 1390\u20131399."},{"key":"e_1_3_2_190_2","doi-asserted-by":"crossref","unstructured":"Haihong Zhao Aochuan Chen Xiangguo Sun Hong Cheng and Jia Li. 2024. All in one and one for all: A simple yet effective method towards cross-domain graph pretraining. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4443\u20134454.","DOI":"10.1145\/3637528.3671913"},{"key":"e_1_3_2_191_2","first-page":"7523","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhao Han","year":"2019","unstructured":"Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon. 2019. On learning invariant representations for domain adaptation. In Proceedings of the International Conference on Machine Learning. PMLR, 7523\u20137532."},{"issue":"10","key":"e_1_3_2_192_2","doi-asserted-by":"crossref","first-page":"11720","DOI":"10.1109\/TPAMI.2023.3272697","article-title":"Dual adaptive representation alignment for cross-domain few-shot learning","volume":"45","author":"Zhao Yifan","year":"2023","unstructured":"Yifan Zhao, Tong Zhang, Jia Li, and Yonghong Tian. 2023. Dual adaptive representation alignment for cross-domain few-shot learning. 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PMLR, 27075\u201327098."},{"issue":"6","key":"e_1_3_2_195_2","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 Bolei","year":"2017","unstructured":"Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2017. Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 6 (2017), 1452\u20131464. Retrieved from http:\/\/data.csail.mit.edu\/places\/places365\/places365standard_easyformat.tar","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_196_2","unstructured":"Fei Zhou Peng Wang Lei Zhang Zhenghua Chen Wei Wei Chen Ding Guosheng Lin and Yanning Zhang. 2025. Meta-exploiting frequency prior for cross-domain few-shot learning. Advances in Neural Information Processing Systems 37 (2025) 116783\u2013116814."},{"key":"e_1_3_2_197_2","article-title":"DiffDet4SAR: Diffusion-based aircraft target detection network for SAR images","author":"Zhou Jie","year":"2024","unstructured":"Jie Zhou, Chao Xiao, Bo Peng, Zhen Liu, Li Liu, Yongxiang Liu, and Xiang Li. 2024. DiffDet4SAR: Diffusion-based aircraft target detection network for SAR images. IEEE Geoscience and Remote Sensing Letters 21 (2024), 1\u20135.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"e_1_3_2_198_2","first-page":"9078","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Zhu Hao","year":"2022","unstructured":"Hao Zhu and Piotr Koniusz. 2022. EASE: Unsupervised discriminant subspace learning for transductive few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 9078\u20139088."},{"issue":"1","key":"e_1_3_2_199_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang Fuzhen","year":"2020","unstructured":"Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. 2020. A comprehensive survey on transfer learning. Proc. IEEE 109, 1 (2020), 43\u201376.","journal-title":"Proc. IEEE"},{"key":"e_1_3_2_200_2","doi-asserted-by":"crossref","first-page":"6368","DOI":"10.1145\/3503161.3548052","volume-title":"Proceedings of the 30th ACM International Conference on Multimedia","author":"Zhuo Linhai","year":"2022","unstructured":"Linhai Zhuo, Yuqian Fu, Jingjing Chen, Yixin Cao, and Yu-Gang Jiang. 2022. TGDM: Target guided dynamic mixup for cross-domain few-shot learning. In Proceedings of the 30th ACM International Conference on Multimedia. 6368\u20136376."},{"key":"e_1_3_2_201_2","unstructured":"Linhai Zhuo Zheng Wang Yuqian Fu and Tianwen Qian. 2024. Prompt as free lunch: Enhancing diversity in source-free cross-domain few-shot learning through semantic-guided prompting. arXiv preprint arXiv:2412.00767 (2024)."},{"key":"e_1_3_2_202_2","first-page":"741","volume-title":"Proceedings of the 29th ACM International Conference on Multimedia","author":"Zou Yixiong","year":"2021","unstructured":"Yixiong Zou, Shanghang Zhang, Jianpeng Yu, Yonghong Tian, and Jos\u00e9 M. F. Moura. 2021. Revisiting mid-level patterns for cross-domain few-shot recognition. 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