{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T16:17:37Z","timestamp":1754151457519,"version":"3.41.2"},"reference-count":68,"publisher":"Association for Computing Machinery (ACM)","issue":"7","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62176172 and 61672364"],"award-info":[{"award-number":["62176172 and 61672364"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2018YFA0701701"],"award-info":[{"award-number":["2018YFA0701701"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>\n            Open-world few-shot classification is restricted by inadequate image-level content representation capabilities when the training and testing sets have significant differences in categories. Recently, many studies show the effectiveness of deep local descriptor-based methods, which attempt to select out dominating contents and discard noisy ones. However, aforementioned methods focus more on external relevance of support and query sets to filter features and ignore internal relevance among support sets, leading to unsatisfying classification performance. To relieve the issue, in this article, we propose the complementary learning Decoupling Category-Region-Aware Network (DCRNet) to simultaneously learn the correlation between internal members and then interact with the external sets. Specifically, we first propose an effective learnable Category Prototype-generated Feature Decoupling Module (CPFDM) to mine co-existing representations and generate comprehensive global class prototype. Then, to adaptively filter out discriminative local descriptors, we present a Category-Aware Selection Module (CASM) and introduce the Category-Aware Contrastive Loss (CACL) to highlight local information that is highly relative to the current category. In addition, the Region-Aware Contrastive Loss (RACL) is designed to encourage the model to concentrate on local regions, yielding powerful ability to distinguish foreground regions from between various categories. Finally, we leverage the filtered support descriptors to adaptively refine query descriptors through the descriptor selection strategy. Extensive experiments demonstrate that the proposed solution outperforms state-of-the-arts on five mainstream general and fine-grained few-shot classification datasets. We have released the training and testing code on\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/jjfang007\/DCRNet\">https:\/\/github.com\/jjfang007\/DCRNet<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3737645","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T10:36:52Z","timestamp":1748428612000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Complementarily Learning Decoupled Category-Region-Aware Prototype for Few-Shot Classification"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8753-2702","authenticated-orcid":false,"given":"Jiajie","family":"Fang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5744-3836","authenticated-orcid":false,"given":"Mengjuan","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5659-7457","authenticated-orcid":false,"given":"Jiaqing","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1762-1757","authenticated-orcid":false,"given":"Bangjun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4318-3081","authenticated-orcid":false,"given":"Fanzhang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01345"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"5763","DOI":"10.1109\/TMM.2022.3198880","article-title":"Learning relative feature displacement for few-shot open-set recognition","volume":"25","author":"Deng Shule","year":"2022","unstructured":"Shule Deng, Jin-Gang Yu, Zihao Wu, Hongxia Gao, Yansheng Li, and Yang Yang. 2022. Learning relative feature displacement for few-shot open-set recognition. IEEE Transactions on Multimedia 25 (2022), 5763\u20135774.","journal-title":"IEEE Transactions on Multimedia"},{"key":"e_1_3_1_6_2","first-page":"21981","article-title":"Crosstransformers: Spatially-aware few-shot transfer","volume":"33","author":"Doersch Carl","year":"2020","unstructured":"Carl Doersch, Ankush Gupta, and Andrew Zisserman. 2020. Crosstransformers: Spatially-aware few-shot transfer. In Advances in Neural Information Processing Systems, Vol. 33, 21981\u201321993.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_7_2","first-page":"716","volume-title":"Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence","author":"Dong Chuanqi","year":"2021","unstructured":"Chuanqi Dong, Wenbin Li, Jing Huo, Zheng Gu, and Yang Gao. 2021. Learning task-aware local representations for few-shot learning. In Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence, 716\u2013722."},{"issue":"4","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","article-title":"One-shot learning of object categories","volume":"28","author":"Fei-Fei Li","year":"2006","unstructured":"Li Fei-Fei, Robert Fergus, and Pietro Perona. 2006. One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 4 (2006), 594\u2013611.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_9_2","first-page":"1126","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the International Conference on Machine Learning. PMLR, 1126\u20131135."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00326"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00815"},{"issue":"2","key":"e_1_3_1_12_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3539576","article-title":"Meta-MMFNet: Meta-learning-based multi-model fusion network for micro-expression recognition","volume":"20","author":"Gong Wenjuan","year":"2023","unstructured":"Wenjuan Gong, Yue Zhang, Wei Wang, Peng Cheng, and Jordi Gonzalez. 2023. Meta-MMFNet: Meta-learning-based multi-model fusion network for micro-expression recognition. ACM Transactions on Multimedia Computing, Communications and Applications 20, 2 (2023), 1\u201320.","journal-title":"ACM Transactions on Multimedia Computing, Communications and Applications"},{"issue":"2","key":"e_1_3_1_13_2","first-page":"1","article-title":"Revisiting local descriptor for improved few-shot classification","volume":"18","author":"He Jun","year":"2022","unstructured":"Jun He, Richang Hong, Xueliang Liu, Mingliang Xu, and Qianru Sun. 2022. Revisiting local descriptor for improved few-shot classification. ACM Transactions on Multimedia Computing, Communications, and Applications 18, 2s (2022), 1\u201323.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_15_2","first-page":"3582","article-title":"Rethinking generalization in few-shot classification","volume":"35","author":"Hiller Markus","year":"2022","unstructured":"Markus Hiller, Rongkai Ma, Mehrtash Harandi, and Tom Drummond. 2022. Rethinking generalization in few-shot classification. In Advances in Neural Information Processing Systems, Vol. 35, 3582\u20133595.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_16_2","first-page":"7171","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Huang Shiyuan","year":"2022","unstructured":"Shiyuan Huang, Jiawei Ma, Guangxing Han, and Shih-Fu Chang. 2022. Task-adaptive negative envision for few-shot open-set recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 7171\u20137180."},{"key":"e_1_3_1_17_2","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00823"},{"key":"e_1_3_1_19_2","unstructured":"Wenbin Li Lei Wang Jing Huo Yinghuan Shi Yang Gao and Jiebo Luo. 2020. Asymmetric distribution measure for few-shot learning. arXiv:2002.00153. Retrieved from https:\/\/arxiv.org\/abs\/2002.00153"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00743"},{"key":"e_1_3_1_21_2","first-page":"8635","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence, Vol","volume":"35","author":"Liu Chen","year":"2021","unstructured":"Chen Liu, Yanwei Fu, Chengming Xu, Siqian Yang, Jilin Li, Chengjie Wang, and Li Zhang. 2021. Learning a few-shot embedding model with contrastive learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 8635\u20138643."},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu Yongcheng","year":"2018","unstructured":"Yongcheng Liu, Bin Fan, Lingfeng Wang, Jun Bai, Shiming Xiang, and Chunhong Pan. 2018. Semantic labeling in very high resolution images via a self-cascaded convolutional neural network. ISPRS Journal of Photogrammetry and Remote Sensing 145 (2018), 78\u201395.","journal-title":". ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"e_1_3_1_23_2","first-page":"14411","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Liu Yang","year":"2022","unstructured":"Yang Liu, Weifeng Zhang, Chao Xiang, Tu Zheng, Deng Cai, and Xiaofei He. 2022. Learning to affiliate: Mutual centralized learning for few-shot classification. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 14411\u201314420."},{"key":"e_1_3_1_24_2","first-page":"1828","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Liu Yang","year":"2022","unstructured":"Yang Liu, Tu Zheng, Jie Song, Deng Cai, and Xiaofei He. 2022. Dmn4: Few-shot learning via discriminative mutual nearest neighbor neural network. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36, 1828\u20131836."},{"key":"e_1_3_1_25_2","first-page":"9011","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"37","author":"Lyu Qiang","year":"2023","unstructured":"Qiang Lyu and Weiqiang Wang. 2023. Compositional prototypical networks for few-shot classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 9011\u20139019."},{"key":"e_1_3_1_26_2","first-page":"10573","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Ma Jiawei","unstructured":"Jiawei Ma, Hanchen Xie, Guangxing Han, Shih-Fu Chang, Aram Galstyan, and Wael Abd-Almageed. 2021. Partner-assisted learning for few-shot image classification. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 10573\u201310582."},{"key":"e_1_3_1_27_2","first-page":"1926","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Ma Rongkai","year":"2022","unstructured":"Rongkai Ma, Pengfei Fang, Tom Drummond, and Mehrtash Harandi. 2022. Adaptive poincar\u00e9 point to set distance for few-shot classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36, 1926\u20131934."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01892"},{"key":"e_1_3_1_29_2","unstructured":"Subhransu Maji Esa Rahtu Juho Kannala Matthew Blaschko and Andrea Vedaldi. 2013. Fine-grained visual classification of aircraft. arXiv:1306.5151. Retrieved from https:\/\/arxiv.org\/abs\/1306.5151"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/978-3-030-86486-6_41","volume-title":"Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference (ECML PKDD \u201921)","author":"Ouali Yassine","year":"2021","unstructured":"Yassine Ouali, C\u00e9line Hudelot, and Myriam Tami. 2021. Spatial contrastive learning for few-shot classification. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference (ECML PKDD \u201921). Springer, 671\u2013686."},{"key":"e_1_3_1_31_2","first-page":"3750","volume-title":"Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP \u201924)","author":"Qiao Qian","year":"2024","unstructured":"Qian Qiao, Yu Xie, Ziyin Zeng, and Fanzhang Li. 2024. TALDS-Net: Task-aware adaptive local descriptors selection for few-shot image classification. In Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP \u201924), 3750\u20133754."},{"key":"e_1_3_1_32_2","volume-title":"International Conference on Learning Representations","author":"Ravi Sachin","year":"2016","unstructured":"Sachin Ravi and Hugo Larochelle. 2016. Optimization as a model for few-shot learning. In International Conference on Learning Representations."},{"key":"e_1_3_1_33_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:1803.00676. Retrieved from https:\/\/arxiv.org\/abs\/1803.00676"},{"key":"e_1_3_1_34_2","first-page":"3093","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Ru Lixiang","year":"2023","unstructured":"Lixiang Ru, Heliang Zheng, Yibing Zhan, and Bo Du. 2023. Token contrast for weakly-supervised semantic segmentation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 3093\u20133102."},{"key":"e_1_3_1_35_2","unstructured":"Eli Schwartz Leonid Karlinsky Joseph Shtok Sivan Harary Mattias Marder Abhishek Kumar Rogerio Feris Raja Giryes and Alex Bronstein. 2018. Delta-encoder: An effective sample synthesis method for few-shot object recognition. In Proceedings of the Annual Conference on Neural Information Processing Systems"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00419"},{"key":"e_1_3_1_37_2","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell Jake","year":"2017","unstructured":"Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. In Advances in Neural Information Processing Systems, Vol. 30.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_38_2","first-page":"645","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Su Jong-Chyi","year":"2020","unstructured":"Jong-Chyi Su, Subhransu Maji, and Bharath Hariharan. 2020. When does self-supervision improve few-shot learning?. In Proceedings of the European Conference on Computer Vision. Springer, 645\u2013666."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01220"},{"key":"e_1_3_1_41_2","first-page":"266","volume-title":"Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920)","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, and Phillip Isola. 2020. Rethinking few-shot image classification: a good embedding is all you need? In Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920). Springer, 266\u2013282."},{"key":"e_1_3_1_42_2","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. arXiv:2001.08735. Retrieved from https:\/\/arxiv.org\/abs\/2001.08735"},{"key":"e_1_3_1_43_2","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals Oriol","year":"2016","unstructured":"Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra, et al. 2016. Matching networks for one shot learning. In Advances in Neural Information Processing Systems, Vol. 29.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_44_2","first-page":"7507","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang Haoyu","year":"2023","unstructured":"Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, and Yanning Zhang. 2023. Glocal energy-based learning for few-shot open-set recognition. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 7507\u20137516."},{"key":"e_1_3_1_45_2","first-page":"9197","volume-title":"In Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Wang Kaixin","year":"2019","unstructured":"Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng. 2019. Panet: Few-shot image semantic segmentation with prototype alignment. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 9197\u20139206."},{"key":"e_1_3_1_46_2","unstructured":"Yan Wang Wei-Lun Chao Kilian Q. Weinberger and Laurens Van Der Maaten. 2019. Simpleshot: Revisiting nearest-neighbor classification for few-shot learning. arXiv:1911.04623. Retrieved from https:\/\/arxiv.org\/abs\/1911.04623"},{"key":"e_1_3_1_47_2","first-page":"5305","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Wang Ze","year":"2023","unstructured":"Ze Wang, Yue Lu, and Qiang Qiu. 2023. Meta-OLE: Meta-learned orthogonal low-rank embedding. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 5305\u20135314."},{"key":"e_1_3_1_48_2","volume":"200","author":"Welinder Peter","year":"2010","unstructured":"Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona. 2010. Caltech-UCSD Birds200 Dataset. California Institute of Technology.","journal-title":"Caltech-UCSD Birds"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00672"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00792"},{"key":"e_1_3_1_51_2","first-page":"2821","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"37","author":"Wu Jijie","year":"2023","unstructured":"Jijie Wu, Dongliang Chang, Aneeshan Sain, Xiaoxu Li, Zhanyu Ma, Jie Cao, Jun Guo, and Yi-Zhe Song. 2023. Bi-directional feature reconstruction network for fine-grained few-shot image classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2821\u20132829."},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00832"},{"key":"e_1_3_1_53_2","first-page":"10275","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Xian Yongqin","unstructured":"Yongqin Xian, Saurabh Sharma, Bernt Schiele, and Zeynep Akata. 2019. f-vaegan-d2: A feature generating framework for any-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 10275\u201310284."},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00781"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614883"},{"key":"e_1_3_1_56_2","first-page":"763","volume-title":"Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920)","author":"Yang Boyu","year":"2020","unstructured":"Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Qixiang Ye. 2020. Prototype mixture models for few-shot semantic segmentation. In Proceedings of the 16th European Conference on Computer Vision (ECCV \u201920). Springer, 763\u2013778."},{"key":"e_1_3_1_57_2","first-page":"293","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Yang Zhanyuan","year":"2022","unstructured":"Zhanyuan Yang, Jinghua Wang, and Yingying Zhu. 2022. Few-shot classification with contrastive learning. In Proceedings of the European Conference on Computer Vision. Springer, 293\u2013309."},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"issue":"2024","key":"e_1_3_1_59_2","first-page":"117","article-title":"Contextualizing meta-learning via learning to decompose","volume":"1","author":"Ye Han-Jia","year":"2024","unstructured":"Han-Jia Ye, Da Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, and De-Chuan Zhan. 2024. Contextualizing meta-learning via learning to decompose. IEEE Transactions on Pattern Analysis and Machine Intelligence 46, 1 (2024), 117\u2013133.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_60_2","first-page":"979","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yu Siyue","year":"2022","unstructured":"Siyue Yu, Jimin Xiao, Bingfeng Zhang, and Eng Gee Lim. 2022. Democracy does matter: Comprehensive feature mining for co-salient object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 979\u2013988."},{"issue":"5","key":"e_1_3_1_61_2","first-page":"5632","article-title":"Deepemd: Differentiable earth mover\u2019s distance for few-shot learning","volume":"45","author":"Zhang Chi","year":"2022","unstructured":"Chi Zhang, Yujun Cai, Guosheng Lin, and Chunhua Shen. 2022. Deepemd: Differentiable earth mover\u2019s distance for few-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 5 (2022), 5632\u20135648.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_62_2","doi-asserted-by":"crossref","first-page":"110158","DOI":"10.1016\/j.patcog.2023.110158","article-title":"Re-abstraction and perturbing support pair network for few-shot fine-grained image classification","volume":"148","author":"Zhang Weichuan","year":"2024","unstructured":"Weichuan Zhang, Yali Zhao, Yongsheng Gao, and Changming Sun. 2024. Re-abstraction and perturbing support pair network for few-shot fine-grained image classification. Pattern Recognition 148 (2024), 110158.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00069"},{"issue":"9","key":"e_1_3_1_64_2","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1109\/TCYB.2020.2992433","article-title":"Sg-one: Similarity guidance network for one-shot semantic segmentation","volume":"50","author":"Zhang Xiaolin","year":"2020","unstructured":"Xiaolin Zhang, Yunchao Wei, Yi Yang, and Thomas S. Huang. 2020. Sg-one: Similarity guidance network for one-shot semantic segmentation. IEEE Transactions on Cybernetics 50, 9 (2020), 3855\u20133865.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00884"},{"issue":"11","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"12816","DOI":"10.1109\/TPAMI.2022.3200865","article-title":"Few-shot class-incremental learning by sampling multi-phase tasks","volume":"45","author":"Zhou Da-Wei","year":"2022","unstructured":"Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, and De-Chuan Zhan. 2022. Few-shot class-incremental learning by sampling multi-phase tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 11 (2022), 12816\u201312831.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"e_1_3_1_68_2","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1109\/TGRS.2020.3006872","article-title":"Class-guided feature decoupling network for airborne image segmentation","volume":"59","author":"Zhou Feng","year":"2021","unstructured":"Feng Zhou, Renlong Hang, and Qingshan Liu. 2021. Class-guided feature decoupling network for airborne image segmentation. IEEE Transactions on Geoscience and Remote Sensing 59, 3, 2020, 2245\u20132255.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00829"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3737645","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T23:53:39Z","timestamp":1752882819000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3737645"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,18]]},"references-count":68,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7,31]]}},"alternative-id":["10.1145\/3737645"],"URL":"https:\/\/doi.org\/10.1145\/3737645","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"type":"print","value":"1551-6857"},{"type":"electronic","value":"1551-6865"}],"subject":[],"published":{"date-parts":[[2025,7,18]]},"assertion":[{"value":"2024-07-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}