{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T22:35:30Z","timestamp":1776465330630,"version":"3.51.2"},"reference-count":109,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s11432-023-4113-1","type":"journal-article","created":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T10:02:22Z","timestamp":1727517742000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Saliency-guided meta-hallucinator for few-shot learning"],"prefix":"10.1007","volume":"67","author":[{"given":"Hongguang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiandong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linru","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piotr","family":"Koniusz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip H. S.","family":"Torr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"4113_CR1","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1037\/h0074898","volume":"8","author":"R S Woodworth","year":"1901","unstructured":"Woodworth R S, Thorndike E L. The influence of improvement in one mental function upon the efficiency of other functions. Psychological Rev, 1901, 8: 247\u2013261","journal-title":"Psychological Rev"},{"key":"4113_CR2","first-page":"464","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"E G Miller","year":"2000","unstructured":"Miller E G, Matsakis N E, Viola P A. Learning from one example through shared densities on transforms. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2000. 464\u2013471"},{"key":"4113_CR3","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","volume":"28","author":"F-F Li","year":"2006","unstructured":"Li F-F, Fergus R, Perona P. One-shot learning of object categories. IEEE Trans Pattern Anal Machine Intell, 2006, 28: 594\u2013611","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"4113_CR4","first-page":"3630","volume-title":"Proceedings of Conference on Neural Information Processing Systems","author":"O Vinyals","year":"2016","unstructured":"Vinyals O, Blundell C, Lillicrap T, et al. Matching networks for one shot learning. In: Proceedings of Conference on Neural Information Processing Systems, 2016. 3630\u20133638"},{"key":"4113_CR5","first-page":"4077","volume-title":"Proceedings of Conference on Neural Information Processing Systems","author":"J Snell","year":"2017","unstructured":"Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning. In: Proceedings of Conference on Neural Information Processing Systems, 2017. 4077\u20134087"},{"key":"4113_CR6","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"F Sung","year":"2018","unstructured":"Sung F, Yang Y, Zhang L, et al. Learning to compare: relation network for few-shot learning. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2018"},{"key":"4113_CR7","first-page":"4967","volume-title":"Proceedings of Conference on Neural Information Processing Systems","author":"A Santoro","year":"2017","unstructured":"Santoro A, Raposo D, Barrett D G, et al. A simple neural network module for relational reasoning. In: Proceedings of Conference on Neural Information Processing Systems, 2017. 4967\u20134976"},{"key":"4113_CR8","first-page":"1185","volume-title":"Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV)","author":"H Zhang","year":"2019","unstructured":"Zhang H, Koniusz P. Power normalizing second-order similarity network for few-shot learning. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2019. 1185\u20131193"},{"key":"4113_CR9","first-page":"1473","volume-title":"Proceedings of Conference on Neural Information Processing Systems","author":"K Q Weinberger","year":"2006","unstructured":"Weinberger K Q, Blitzer J, Saul L K. Distance metric learning for large margin nearest neighbor classification. In: Proceedings of Conference on Neural Information Processing Systems, 2006. 1473\u20131480"},{"key":"4113_CR10","first-page":"2288","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"M K\u00f6stinger","year":"2012","unstructured":"K\u00f6stinger M, Hirzer M, Wohlhart P, et al. Large scale metric learning from equivalence constraints. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2012. 2288\u20132295"},{"key":"4113_CR11","first-page":"1404","volume-title":"Proceedings of International Conference on Machine Learning","author":"M Harandi","year":"2017","unstructured":"Harandi M, Salzmann M, Hartley R. Joint dimensionality reduction and metric learning: a geometric take. In: Proceedings of International Conference on Machine Learning, 2017. 1404\u20131413"},{"key":"4113_CR12","first-page":"1126","volume-title":"Proceedings of International Conference on Machine Learning","author":"C Finn","year":"2017","unstructured":"Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of International Conference on Machine Learning, 2017. 1126\u20131135"},{"key":"4113_CR13","first-page":"10657","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"K Lee","year":"2019","unstructured":"Lee K, Maji S, Ravichandran A, et al. Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019. 10657\u201310665"},{"key":"4113_CR14","unstructured":"Wang Y, Chao W L, Weinberger K Q, et al. SimpleShot: revisiting nearest-neighbor classification for few-shot learning. 2019. ArXiv:1911.04623"},{"key":"4113_CR15","first-page":"11660","volume-title":"Proceedings of International Conference on Machine Learning","author":"I Ziko","year":"2020","unstructured":"Ziko I, Dolz J, Granger E, et al. Laplacian regularized few-shot learning. In: Proceedings of International Conference on Machine Learning, 2020. 11660\u201311670"},{"key":"4113_CR16","first-page":"3037","volume-title":"Proceedings of International Conference on Computer Vision","author":"B Hariharan","year":"2017","unstructured":"Hariharan B, Girshick R B. Low-shot visual recognition by shrinking and hallucinating features. In: Proceedings of International Conference on Computer Vision, 2017. 3037\u20133046"},{"key":"4113_CR17","doi-asserted-by":"crossref","unstructured":"Wang Y X, Girshick R, Hebert M, et al. Low-shot learning from imaginary data. 2018. ArXiv:1801.05401","DOI":"10.1109\/CVPR.2018.00760"},{"key":"4113_CR18","first-page":"31","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"R Zhang","year":"2018","unstructured":"Zhang R, Che T, Ghahramani Z, et al. MetaGAN: an adversarial approach to few-shot learning. In: Proceedings of Advances in Neural Information Processing Systems, 2018. 31"},{"key":"4113_CR19","first-page":"13470","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"K Li","year":"2020","unstructured":"Li K, Zhang Y, Li K, et al. Adversarial feature hallucination networks for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020. 13470\u201313479"},{"key":"4113_CR20","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","author":"P Mangla","year":"2020","unstructured":"Mangla P, Kumari N, Sinha A, et al. Charting the right manifold: manifold mixup for few-shot learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 2020"},{"key":"4113_CR21","doi-asserted-by":"publisher","first-page":"5326","DOI":"10.1145\/3474085.3475655","volume-title":"Proceedings of the 29th ACM International Conference on Multimedia","author":"Y Fu","year":"2021","unstructured":"Fu Y, Fu Y, Jiang Y G. Meta-FDMixup: cross-domain few-shot learning guided by labeled target data. In: Proceedings of the 29th ACM International Conference on Multimedia, 2021. 5326\u20135334"},{"key":"4113_CR22","volume-title":"Proceedings of International Conference on Learning Representations","author":"H Zhang","year":"2018","unstructured":"Zhang H, Cisse M, Dauphin Y N, et al. mixup: beyond empirical risk minimization. In: Proceedings of International Conference on Learning Representations, 2018"},{"key":"4113_CR23","first-page":"6438","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"V Verma","year":"2019","unstructured":"Verma V, Lamb A, Beckham C, et al. Manifold Mixup: better representations by interpolating hidden states. In: Proceedings of the 36th International Conference on Machine Learning, 2019. 6438\u20136447"},{"key":"4113_CR24","first-page":"2770","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"H Zhang","year":"2019","unstructured":"Zhang H, Zhang J, Koniusz P. Few-shot learning via saliency-guided hallucination of samples. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2019. 2770\u20132779"},{"key":"4113_CR25","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"J Zhang","year":"2018","unstructured":"Zhang J, Zhang T, Dai Y, et al. Deep unsupervised saliency detection: a multiple noisy labeling perspective. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2018"},{"key":"4113_CR26","first-page":"1","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"T Liu","year":"2007","unstructured":"Liu T, Sun J, Zheng N N, et al. Learning to detect a salient object. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2007. 1\u20138"},{"key":"4113_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-39935-3","volume-title":"Neural Information Processing: Research and Development","author":"J C Rajapakse","year":"2004","unstructured":"Rajapakse J C, Wang L. Neural Information Processing: Research and Development. Berlin: Springer, 2004"},{"key":"4113_CR28","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis, 2015, 115: 211\u2013252","journal-title":"Int J Comput Vis"},{"key":"4113_CR29","volume-title":"Proceedings of AAAI Conference on Artificial Intelligence","author":"H Larochelle","year":"2008","unstructured":"Larochelle H, Erhan D, Bengio Y. Zero-data learning of new tasks. In: Proceedings of AAAI Conference on Artificial Intelligence, 2008"},{"key":"4113_CR30","first-page":"1778","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"A Farhadi","year":"2009","unstructured":"Farhadi A, Endres I, Hoiem D, et al. Describing objects by their attributes. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2009. 1778\u20131785"},{"key":"4113_CR31","first-page":"819","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"Z Akata","year":"2013","unstructured":"Akata Z, Perronnin F, Harchaoui Z, et al. Label-embedding for attribute-based classification. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2013. 819\u2013826"},{"key":"4113_CR32","first-page":"7670","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"H Zhang","year":"2018","unstructured":"Zhang H, Koniusz P. Zero-shot kernel learning. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2018. 7670\u20137679"},{"key":"4113_CR33","doi-asserted-by":"publisher","first-page":"9596","DOI":"10.1073\/pnas.092277599","volume":"99","author":"F F Li","year":"2002","unstructured":"Li F F, VanRullen R, Koch C, et al. Rapid natural scene categorization in the near absence of attention. Proc Natl Acad Sci USA, 2002, 99: 9596\u20139601","journal-title":"Proc Natl Acad Sci USA"},{"key":"4113_CR34","first-page":"449","volume-title":"Proceedings of Conference on Neural Information Processing Systems","author":"M Fink","year":"2005","unstructured":"Fink M. Object classification from a single example utilizing class relevance metrics. In: Proceedings of Conference on Neural Information Processing Systems, 2005. 449\u2013456"},{"key":"4113_CR35","first-page":"672","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"E Bart","year":"2005","unstructured":"Bart E, Ullman S. Cross-generalization: learning novel classes from a single example by feature replacement. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2005. 672\u2013679"},{"key":"4113_CR36","volume-title":"Proceedings of the Annual Meeting of the Cognitive Science Society","author":"B M Lake","year":"2011","unstructured":"Lake B M, Salakhutdinov R, Gross J, et al. One shot learning of simple visual concepts. In: Proceedings of the Annual Meeting of the Cognitive Science Society, 2011"},{"key":"4113_CR37","volume-title":"Proceedings of International Conference on Machine Learning Deep Learning Workshop","author":"G Koch","year":"2015","unstructured":"Koch G, Zemel R, Salakhutdinov R. Siamese neural networks for one-shot image recognition. In: Proceedings of International Conference on Machine Learning Deep Learning Workshop, 2015"},{"key":"4113_CR38","doi-asserted-by":"crossref","unstructured":"Tian Y, Wang Y, Krishnan D, et al. Rethinking few-shot image classification: a good embedding is all you need? 2020. ArXiv:2003.11539","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"4113_CR39","doi-asserted-by":"crossref","unstructured":"Zhang C, Cai Y, Lin G, et al. DeepEMD: few-shot image classification with differentiable earth mover\u2019s distance and structured classifiers. 2020. ArXiv:2003.06777","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"4113_CR40","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Commun ACM, 2020, 63: 139\u2013144","journal-title":"Commun ACM"},{"key":"4113_CR41","unstructured":"Karras T, Aila T, Laine S, et al. Progressive growing of gans for improved quality, stability, and variation. 2017. ArXiv:1710.10196"},{"key":"4113_CR42","unstructured":"Brock A, Donahue J, Simonyan K. Large scale gan training for high fidelity natural image synthesis. 2018. ArXiv: 1809.11096"},{"key":"4113_CR43","first-page":"4401","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"T Karras","year":"2019","unstructured":"Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 4401\u20134410"},{"key":"4113_CR44","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"J Zhang","year":"2019","unstructured":"Zhang J, Zhao C, Ni B, et al. Variational few-shot learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 2019"},{"key":"4113_CR45","first-page":"214","volume-title":"Proceedings of International Conference on Machine Learning","author":"M Arjovsky","year":"2017","unstructured":"Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks. In: Proceedings of International Conference on Machine Learning, 2017. 214\u2013223"},{"key":"4113_CR46","first-page":"10551","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"M Y Liu","year":"2019","unstructured":"Liu M Y, Huang X, Mallya A, et al. Few-shot unsupervised image-to-image translation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019. 10551\u201310560"},{"key":"4113_CR47","first-page":"8680","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Z Chen","year":"2019","unstructured":"Chen Z, Fu Y, Wang Y X, et al. Image deformation meta-networks for one-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 8680\u20138689"},{"key":"4113_CR48","first-page":"3963","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Q Luo","year":"2021","unstructured":"Luo Q, Wang L, Lv J, et al. Few-shot learning via feature hallucination with variational inference. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2021. 3963\u20133972"},{"key":"4113_CR49","first-page":"3500","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"M Lazarou","year":"2022","unstructured":"Lazarou M, Stathaki T, Avrithis Y. Tensor feature hallucination for few-shot learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022. 3500\u20133510"},{"key":"4113_CR50","first-page":"2814","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"W Zhu","year":"2014","unstructured":"Zhu W, Liang S, Wei Y, et al. Saliency optimization from robust background detection. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2014. 2814\u20132821"},{"key":"4113_CR51","first-page":"825","volume-title":"Proceedings of European Conference on Computer Vision","author":"L Wang","year":"2016","unstructured":"Wang L, Wang L, Lu H, et al. Saliency detection with recurrent fully convolutional networks. In: Proceedings of European Conference on Computer Vision, 2016. 825\u2013841"},{"key":"4113_CR52","first-page":"3203","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"Q Hou","year":"2017","unstructured":"Hou Q, Cheng M M, Hu X, et al. Deeply supervised salient object detection with short connections. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2017. 3203\u20133212"},{"key":"4113_CR53","first-page":"3987","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"C Yang","year":"2021","unstructured":"Yang C, Wu Z, Zhou B, et al. Instance localization for self-supervised detection pretraining. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021. 3987\u20133996"},{"key":"4113_CR54","first-page":"123","volume-title":"Proceedings of European Conference on Computer Vision","author":"O J H\u00e9naff","year":"2022","unstructured":"H\u00e9naff O J, Koppula S, Shelhamer E, et al. Object discovery and representation networks. In: Proceedings of European Conference on Computer Vision, 2022. 123\u2013143"},{"key":"4113_CR55","first-page":"10990","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"N Zhao","year":"2021","unstructured":"Zhao N, Wu Z, Lau R W, et al. Distilling localization for self-supervised representation learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2021. 10990\u201310998"},{"key":"4113_CR56","first-page":"16684","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Z Xie","year":"2021","unstructured":"Xie Z, Lin Y, Zhang Z, et al. Propagate yourself: exploring pixel-level consistency for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021. 16684\u201316693"},{"key":"4113_CR57","first-page":"5761","volume":"44","author":"J Zhang","year":"2021","unstructured":"Zhang J, Fan D-P, Dai Y C, et al. Uncertainty inspired RGB-D saliency detection. IEEE Trans Pattern Anal Mach Intell, 2021, 44: 5761\u20135779","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4113_CR58","first-page":"3971","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"G Shin","year":"2022","unstructured":"Shin G, Albanie S, Xie W. Unsupervised salient object detection with spectral cluster voting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022. 3971\u20133980"},{"key":"4113_CR59","first-page":"3176","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"O Sim\u00e9oni","year":"2023","unstructured":"Sim\u00e9oni O, Sekkat C, Puy G, et al. Unsupervised object localization: observing the background to discover objects. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023. 3176\u20133186"},{"key":"4113_CR60","first-page":"33371","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"A Bielski","year":"2022","unstructured":"Bielski A, Favaro P. MOVE: unsupervised movable object segmentation and detection. In: Proceedings of Advances in Neural Information Processing Systems, 2022. 35: 33371\u201333386"},{"key":"4113_CR61","volume-title":"Proceedings of European Conference on Computer Vision","author":"O Tuzel","year":"2006","unstructured":"Tuzel O, Porikli F, Meer P. Region covariance: a fast descriptor for detection and classification. In: Proceedings of European Conference on Computer Vision, 2006"},{"key":"4113_CR62","volume-title":"Proceedings of the 6th International Conference on Computer Vision\/Computer Graphics Collaboration Techniques and Applications","author":"A Romero","year":"2013","unstructured":"Romero A, Ter\u00e1n M Y, Gouiff\u00e8s M, et al. Enhanced local binary covariance matrices (ELBCM) for texture analysis and object tracking. In: Proceedings of the 6th International Conference on Computer Vision\/Computer Graphics Collaboration Techniques and Applications, 2013"},{"key":"4113_CR63","volume-title":"Proceedings of European Conference on Computer Vision","author":"J Carreira","year":"2012","unstructured":"Carreira J, Caseiro R, Batista J, et al. Semantic segmentation with second-order pooling. In: Proceedings of European Conference on Computer Vision, 2012"},{"key":"4113_CR64","series-title":"Technical Report","volume-title":"Higher-order Occurrence Pooling on Mid- and Low-level Features: Visual Concept Detection","author":"P Koniusz","year":"2013","unstructured":"Koniusz P, Yan F, Gosselin P, et al. Higher-order Occurrence Pooling on Mid- and Low-level Features: Visual Concept Detection. Technical Report, 2013"},{"key":"4113_CR65","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1109\/TPAMI.2016.2545667","volume":"39","author":"P Koniusz","year":"2017","unstructured":"Koniusz P, Yan F, Gosselin P H, et al. Higher-order occurrence pooling for bags-of-words: visual concept detection. IEEE Trans Pattern Anal Mach Intell, 2017, 39: 313\u2013326","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4113_CR66","volume-title":"Proceedings of International Conference on Computer Vision","author":"T Y Lin","year":"2017","unstructured":"Lin T Y, Chowdhury A R, Maji S. Bilinear cnn models for fine-grained visual recognition. In: Proceedings of International Conference on Computer Vision, 2017"},{"key":"4113_CR67","volume-title":"Proceedings of International Conference on Computer Vision","author":"G Hu","year":"2017","unstructured":"Hu G, Hua Y, Yuan Y, et al. Attribute-enhanced face recognition with neural tensor fusion networks. In: Proceedings of International Conference on Computer Vision, 2017"},{"key":"4113_CR68","first-page":"1169","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"H J\u00e9gou","year":"2009","unstructured":"J\u00e9gou H, Douze M, Schmid C. On the burstiness of visual elements. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2009. 1169\u20131176"},{"key":"4113_CR69","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.cviu.2012.10.010","volume":"117","author":"P Koniusz","year":"2013","unstructured":"Koniusz P, Yan F, Mikolajczyk K. Comparison of mid-level feature coding approaches and pooling strategies in visual concept detection. Comput Vision Image Understanding, 2013, 117: 479\u2013492","journal-title":"Comput Vision Image Understanding"},{"key":"4113_CR70","first-page":"5774","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"P Koniusz","year":"2018","unstructured":"Koniusz P, Zhang H, Porikli F. A deeper look at power normalizations. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2018. 5774\u20135783"},{"key":"4113_CR71","volume-title":"Proceedings of International Conference on Machine Learning","author":"Y Boureau","year":"2010","unstructured":"Boureau Y, Ponce J, LeCun Y. A theoretical analysis of feature pooling in vision algorithms. In: Proceedings of International Conference on Machine Learning, 2010"},{"key":"4113_CR72","first-page":"7972","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"J Xie","year":"2022","unstructured":"Xie J, Long F, Lv J, et al. Joint distribution matters: deep Brownian distance covariance for few-shot classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022. 7972\u20137981"},{"key":"4113_CR73","first-page":"2582","volume":"43","author":"Q Wang","year":"2020","unstructured":"Wang Q, Xie J, Zuo W, et al. Deep CNNs meet global covariance pooling: better representation and generalization. IEEE Trans Pattern Anal Mach Intell, 2020, 43: 2582\u20132597","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4113_CR74","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1023\/A:1022643204877","volume":"1","author":"J R Quinlan","year":"1986","unstructured":"Quinlan J R. Induction of decision trees. Mach Learn, 1986, 1: 81\u2013106","journal-title":"Mach Learn"},{"key":"4113_CR75","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn, 1995, 20: 273\u2013297","journal-title":"Mach Learn"},{"key":"4113_CR76","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-3-319-14142-8_8","volume-title":"Proceedings of Data Mining","author":"C C Aggarwal","year":"2015","unstructured":"Aggarwal C C. Outlier analysis. In: Proceedings of Data Mining, 2015. 237\u2013263"},{"key":"4113_CR77","unstructured":"Pang G, Shen C, Cao L, et al. Deep learning for anomaly detection: a review. 2020. ArXiv:2007.02500"},{"key":"4113_CR78","first-page":"5962","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"S Wang","year":"2019","unstructured":"Wang S, Zeng Y, Liu X, et al. Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network. In: Proceedings of Advances in Neural Information Processing Systems, 2019. 5962\u20135975"},{"key":"4113_CR79","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"G E Hinton","year":"2006","unstructured":"Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313: 504\u2013507","journal-title":"Science"},{"key":"4113_CR80","first-page":"2672","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. In: Proceedings of Advances in Neural Information Processing Systems, 2014. 2672\u20132680"},{"key":"4113_CR81","first-page":"4393","volume-title":"Proceedings of International Conference on Machine Learning","author":"L Ruff","year":"2018","unstructured":"Ruff L, Vandermeulen R, Goernitz N, et al. Deep one-class classification. In: Proceedings of International Conference on Machine Learning, 2018. 4393\u20134402"},{"key":"4113_CR82","first-page":"9758","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"I Golan","year":"2018","unstructured":"Golan I, El-Yaniv R. Deep anomaly detection using geometric transformations. In: Proceedings of Advances in Neural Information Processing Systems, 2018. 9758\u20139769"},{"key":"4113_CR83","doi-asserted-by":"crossref","unstructured":"Tian Y, Maicas G, Pu L Z C T, et al. Few-shot anomaly detection for polyp frames from colonoscopy. 2020. ArXiv:2006.14811","DOI":"10.1007\/978-3-030-59725-2_27"},{"key":"4113_CR84","unstructured":"Kruspe A. One-way prototypical networks. 2019. ArXiv:1906.00820"},{"key":"4113_CR85","unstructured":"Frikha A, Krompa\u00df D, K\u00f6pken H G, et al. Few-shot one-class classification via meta-learning. 2020. ArXiv:2007.04146"},{"key":"4113_CR86","unstructured":"Hjelm R D, Fedorov A, Lavoie-Marchildon S, et al. Learning deep representations by mutual information estimation and maximization. 2018. ArXiv:1808.06670"},{"key":"4113_CR87","first-page":"409","volume-title":"Proceedings of Conference on Computer Vision and Pattern Recognition","author":"M Cheng","year":"2011","unstructured":"Cheng M, Zhang G, Mitra N, et al. Global contrast based salient region detection. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2011. 409\u2013416"},{"key":"4113_CR88","doi-asserted-by":"publisher","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","volume":"24","author":"A Borji","year":"2015","unstructured":"Borji A, Cheng M M, Jiang H, et al. Salient object detection: a benchmark. IEEE Trans Image Process, 2015, 24: 5706\u20135722","journal-title":"IEEE Trans Image Process"},{"key":"4113_CR89","volume-title":"Proceedings of the 6th International Conference on Learning Representations","author":"M Ren","year":"2018","unstructured":"Ren M, Triantafillou E, Ravi S, et al. Meta-learning for semi-supervised few-shot classification. In: Proceedings of the 6th International Conference on Learning Representations, 2018"},{"key":"4113_CR90","series-title":"Technical Report CNS-TR-2011-001","volume-title":"The Caltech-UCSD Birds-200-2011 Dataset","author":"C Wah","year":"2011","unstructured":"Wah C, Branson S, Welinder P, et al. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011"},{"key":"4113_CR91","volume-title":"Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing","author":"M E Nilsback","year":"2008","unstructured":"Nilsback M E, Zisserman A. Automated flower classification over a large number of classes. In: Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, 2008"},{"key":"4113_CR92","volume-title":"Proceedings of European Conference on Computer Vision","author":"L Bossard","year":"2014","unstructured":"Bossard L, Guillaumin M, van Gool L. Food-101-mining discriminative components with random forests. In: Proceedings of European Conference on Computer Vision, 2014"},{"key":"4113_CR93","doi-asserted-by":"crossref","unstructured":"Koniusz P, Tas Y, Zhang H, et al. Museum exhibit identification challenge for the supervised domain adaptation. 2018. ArXiv:1802.01093","DOI":"10.1007\/978-3-030-01270-0_48"},{"key":"4113_CR94","unstructured":"Koniusz P, Zhang H. Power normalizations in fine-grained image, few-shot image and graph classification. 2020. ArXiv:2012.13975"},{"key":"4113_CR95","volume-title":"Proceedings of International Conference on Learning Representations","author":"S Ravi","year":"2017","unstructured":"Ravi S, Larochelle H. Optimization as a model for few-shot learning. In: Proceedings of International Conference on Learning Representations, 2017"},{"key":"4113_CR96","unstructured":"Oreshkin B N, Rodriguez P, Lacoste A. TADAM: task dependent adaptive metric for improved few-shot learning. 2018. ArXiv:1805.10123"},{"key":"4113_CR97","unstructured":"Mishra N, Rohaninejad M, Chen X, et al. A simple neural attentive meta-learner. 2017. ArXiv:1707.03141"},{"key":"4113_CR98","first-page":"403","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Q Sun","year":"2019","unstructured":"Sun Q, Liu Y, Chua T S, et al. Meta-transfer learning for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019. 403\u2013412"},{"key":"4113_CR99","first-page":"8402","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Z Zhou","year":"2021","unstructured":"Zhou Z, Qiu X, Xie J, et al. Binocular mutual learning for improving few-shot classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021. 8402\u20138411"},{"key":"4113_CR100","first-page":"13073","volume":"34","author":"X Luo","year":"2021","unstructured":"Luo X, Wei L, Wen L, et al. Rectifying the shortcut learning of background for few-shot learning. In: Proceedings of Advances in Neural Information Processing Systems, 2021. 34: 13073\u201313085","journal-title":"Proceedings of Advances in Neural Information Processing Systems"},{"key":"4113_CR101","first-page":"8433","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"J Wu","year":"2021","unstructured":"Wu J, Zhang T, Zhang Y, et al. Task-aware part mining network for few-shot learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021. 8433\u20138442"},{"key":"4113_CR102","first-page":"10981","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"J Zhao","year":"2021","unstructured":"Zhao J, Yang Y, Lin X, et al. Looking wider for better adaptive representation in few-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2021. 10981\u201310989"},{"key":"4113_CR103","first-page":"10573","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"J Ma","year":"2021","unstructured":"Ma J, Xie H, Han G, et al. Partner-assisted learning for few-shot image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021. 10573\u201310582"},{"key":"4113_CR104","first-page":"9014","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"A Afrasiyabi","year":"2022","unstructured":"Afrasiyabi A, Larochelle H, Lalonde J F, et al. Matching feature sets for few-shot image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022. 9014\u20139024"},{"key":"4113_CR105","doi-asserted-by":"crossref","unstructured":"Lai J, Yang S, Zhou J, et al. Clustered-patch element connection for few-shot learning. 2023. ArXiv:2304.10093","DOI":"10.24963\/ijcai.2023\/110"},{"key":"4113_CR106","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1109\/TMM.2020.3001510","volume":"23","author":"H Huang","year":"2021","unstructured":"Huang H, Zhang J, Zhang J, et al. Low-rank pairwise alignment bilinear network for few-shot fine-grained image classification. IEEE Trans Multimedia, 2021, 23: 1666\u20131680","journal-title":"IEEE Trans Multimedia"},{"key":"4113_CR107","first-page":"1090","volume-title":"Proceedings of International Joint Conferences on Artificial Intelligence","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Liu C, Jiang S. Multi-attention meta learning for few-shot fine-grained image recognition. In: Proceedings of International Joint Conferences on Artificial Intelligence, 2020. 1090\u20131096"},{"key":"4113_CR108","first-page":"52","volume-title":"Proceedings of International Conference on Artificial Neural Networks","author":"J Masci","year":"2011","unstructured":"Masci J, Meier U, Cire\u015fan D, et al. Stacked convolutional auto-encoders for hierarchical feature extraction. In: Proceedings of International Conference on Artificial Neural Networks, 2011. 52\u201359"},{"key":"4113_CR109","first-page":"146","volume-title":"Proceedings of International Conference on Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","unstructured":"Schlegl T, Seeb\u00f6ck P, Waldstein S M, et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Proceedings of International Conference on Information Processing in Medical Imaging, 2017. 146\u2013157"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-4113-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-023-4113-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-4113-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T22:02:52Z","timestamp":1763589772000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-023-4113-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,26]]},"references-count":109,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["4113"],"URL":"https:\/\/doi.org\/10.1007\/s11432-023-4113-1","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,26]]},"assertion":[{"value":"16 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"202103"}}