{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T04:45:27Z","timestamp":1784349927810,"version":"3.55.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11432-020-3156-7","type":"journal-article","created":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T15:27:47Z","timestamp":1611329267000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Task-wise attention guided part complementary learning for few-shot image classification"],"prefix":"10.1007","volume":"64","author":[{"given":"Gong","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruimin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunbo","family":"Lang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junwei","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,20]]},"reference":[{"key":"3156_CR1","unstructured":"Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of Advances in Neural Information Processing Systems, 2015. 91\u201399"},{"key":"3156_CR2","doi-asserted-by":"crossref","unstructured":"Lin T Y, Doll\u00e1r P, Girshick R, et al. Feature pyramid networks for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017. 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"3156_CR3","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016. 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"3156_CR4","doi-asserted-by":"crossref","unstructured":"Cheng G, Zhou P C, Han J W. RIFD-CNN: rotation-invariant and fisher discriminative convolutional neural networks for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016. 2884\u20132893","DOI":"10.1109\/CVPR.2016.315"},{"key":"3156_CR5","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector. In: Proceedings of European Conference on Computer Vision, 2016. 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"3156_CR6","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1109\/TIP.2018.2867198","volume":"28","author":"G Cheng","year":"2019","unstructured":"Cheng G, Han J, Zhou P, et al. Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection. IEEE Trans Image Process, 2019, 28: 265\u2013278","journal-title":"IEEE Trans Image Process"},{"key":"3156_CR7","doi-asserted-by":"crossref","unstructured":"He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3156_CR8","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. ArXiv:1409.1556"},{"key":"3156_CR9","doi-asserted-by":"publisher","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","volume":"56","author":"G Cheng","year":"2018","unstructured":"Cheng G, Yang C Y, Yao X W, et al. When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs. IEEE Trans Geosci Remote Sens, 2018, 56: 2811\u20132821","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"3156_CR10","doi-asserted-by":"crossref","unstructured":"Cheng G, Gao D C, Liu Y, et al. Multi-scale and discriminative part detectors based features for multi-label image classification. In: Proceedings of International Joint Conference on Artificial Intelligence, 2018. 649\u2013655","DOI":"10.24963\/ijcai.2018\/90"},{"key":"3156_CR11","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015. 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"3156_CR12","doi-asserted-by":"crossref","unstructured":"Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In: Proceedings of IEEE International Conference on Computer Vision, 2015. 1520\u20131528","DOI":"10.1109\/ICCV.2015.178"},{"key":"3156_CR13","doi-asserted-by":"publisher","first-page":"107448","DOI":"10.1016\/j.patcog.2020.107448","volume":"106","author":"N Wang","year":"2020","unstructured":"Wang N, Ma S H, Li J Y, et al. Multistage attention network for image inpainting. Pattern Recogn, 2020, 106: 107448","journal-title":"Pattern Recogn"},{"key":"3156_CR14","doi-asserted-by":"publisher","first-page":"107173","DOI":"10.1016\/j.patcog.2019.107173","volume":"102","author":"L C Song","year":"2020","unstructured":"Song L C, Wang C, Zhang L F, et al. Unsupervised domain adaptive re-identification: theory and practice. Pattern Recogn, 2020, 102: 107173","journal-title":"Pattern Recogn"},{"key":"3156_CR15","doi-asserted-by":"publisher","first-page":"6116","DOI":"10.1109\/TIP.2019.2924811","volume":"28","author":"X S Wei","year":"2019","unstructured":"Wei X S, Wang P, Liu L Q, et al. Piecewise classifier mappings: learning fine-grained learners for novel categories with few examples. IEEE Trans Image Process, 2019, 28: 6116\u20136125","journal-title":"IEEE Trans Image Process"},{"key":"3156_CR16","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.patrec.2020.07.015","volume":"140","author":"Z Ji","year":"2020","unstructured":"Ji Z, Chai X L, Yu Y L, et al. Improved prototypical networks for few-shot learning. Pattern Recogn Lett, 2020, 140: 81\u201387","journal-title":"Pattern Recogn Lett"},{"key":"3156_CR17","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1109\/TNNLS.2019.2904991","volume":"31","author":"Z Ji","year":"2020","unstructured":"Ji Z, Sun Y X, Yu Y L, et al. Attribute-guided network for cross-modal zero-shot hashing. IEEE Trans Neur Netw Lear Syst, 2020, 31: 321\u2013330","journal-title":"IEEE Trans Neur Netw Lear Syst"},{"key":"3156_CR18","unstructured":"Wang Y Q, Yao Q M, Kwok J T, et al. Generalizing from a few examples: a survey on few-shot learning. 2019. ArXiv:1904.05046"},{"key":"3156_CR19","doi-asserted-by":"publisher","first-page":"120101","DOI":"10.1007\/s11432-020-3032-8","volume":"64","author":"Z Ji","year":"2021","unstructured":"Ji Z, Yan J T, Wang Q, et al. Triple discriminator generative adversarial network for zero-shot image classification. Sci China Inf Sci, 2021, 64: 120101","journal-title":"Sci China Inf Sci"},{"key":"3156_CR20","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1023\/A:1019956318069","volume":"18","author":"R Vilalta","year":"2002","unstructured":"Vilalta R, Drissi Y. A perspective view and survey of meta-learning. Artif Intell Rev, 2002, 18: 77\u201395","journal-title":"Artif Intell Rev"},{"key":"3156_CR21","unstructured":"Bertinetto L, Henriques J F, Torr P H, et al. Meta-learning with differentiable closed-form solvers. In: Proceedings of International Conference on Learning Representations, 2019. 1\u201315"},{"key":"3156_CR22","unstructured":"Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 4077\u20134087"},{"key":"3156_CR23","unstructured":"Vinyals O, Blundell C, Lillicrap T, et al. Matching networks for one shot learning. In: Proceedings of Advances in Neural Information Processing Systems, 2016. 3630\u20133638"},{"key":"3156_CR24","unstructured":"Andrychowicz M, Denil M, Gomez S, et al. Learning to learn by gradient descent by gradient descent. In: Proceedings of Advances in Neural Information Processing Systems, 2016. 3981\u20133989"},{"key":"3156_CR25","unstructured":"Ravi S, Larochelle H. Optimization as a model for few-shot learning. In: Proceedings of International Conference on Learning Representations, 2017. 1\u201311"},{"key":"3156_CR26","unstructured":"Santoro A, Bartunov S, Botvinick M, et al. Meta-learning with memory-augmented neural networks. In: Proceedings of the 33rd International Conference on Machine Learning, 2016. 1842\u20131850"},{"key":"3156_CR27","unstructured":"Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, 2017. 1126\u20131135"},{"key":"3156_CR28","unstructured":"Li Z G, Zhou F W, Chen F, et al. Meta-SGD: learning to learn quickly for few-shot learning. 2017. ArXiv:1707.09835"},{"key":"3156_CR29","doi-asserted-by":"crossref","unstructured":"Jamal M, Qi G J. Task agnostic meta-learning for few-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 11719\u201311727","DOI":"10.1109\/CVPR.2019.01199"},{"key":"3156_CR30","unstructured":"Zhou F W, Wu B, Li Z G. Deep meta-learning: learning to learn in the concept space. 2018. ArXiv:1802.03596"},{"key":"3156_CR31","doi-asserted-by":"crossref","unstructured":"Sun Q R, Liu Y Y, Chua T, et al. Meta-transfer learning for few-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 403\u2013412","DOI":"10.1109\/CVPR.2019.00049"},{"key":"3156_CR32","doi-asserted-by":"crossref","unstructured":"Lee K, Maji S, Ravichandran A, et al. Meta-learning with differentiable convex optimization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 10657\u201310665","DOI":"10.1109\/CVPR.2019.01091"},{"key":"3156_CR33","doi-asserted-by":"crossref","unstructured":"Lifchitz Y, Avrithis Y, Picard S, et al. Dense classification and implanting for few-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 9258\u20139267","DOI":"10.1109\/CVPR.2019.00948"},{"key":"3156_CR34","unstructured":"Munkhdalai T, Yu H. Meta networks. In: Proceedings of the 34th International Conference on Machine Learning, 2017. 2554\u20132563"},{"key":"3156_CR35","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y X, Zhang L, et al. Learning to compare: relation network for few-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018. 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"3156_CR36","doi-asserted-by":"crossref","unstructured":"Wang P, Liu L Q, Shen C H, et al. Multi-attention network for one shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017. 2721\u20132729","DOI":"10.1109\/CVPR.2017.658"},{"key":"3156_CR37","first-page":"8642","volume":"33","author":"W B Li","year":"2019","unstructured":"Li W B, Xu J L, Huo J, et al. Distribution consistency based covariance metric networks for few-shot learning. Assoc Adv Artif Intell, 2019, 33: 8642\u20138649","journal-title":"Assoc Adv Artif Intell"},{"key":"3156_CR38","doi-asserted-by":"crossref","unstructured":"Li W B, Wang L, Xu J L, et al. Revisiting local descriptor based image-to-class measure for few-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 7260\u20137268","DOI":"10.1109\/CVPR.2019.00743"},{"key":"3156_CR39","doi-asserted-by":"crossref","unstructured":"Li H Y, Eigen D, Dodge S, et al. Finding task-relevant features for few-shot learning by category traversal. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 1\u201310","DOI":"10.1109\/CVPR.2019.00009"},{"key":"3156_CR40","doi-asserted-by":"crossref","unstructured":"Zhang H G, Zhang J, Koniusz P. Few-shot learning via saliency-guided hallucination of samples. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 2770\u20132779","DOI":"10.1109\/CVPR.2019.00288"},{"key":"3156_CR41","doi-asserted-by":"crossref","unstructured":"Alfassy A, Karlinsky L, Aides A, et al. LaSO: label-set operations networks for multi-label few-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 6548\u20136557","DOI":"10.1109\/CVPR.2019.00671"},{"key":"3156_CR42","doi-asserted-by":"crossref","unstructured":"Chen Z T, Fu Y W, Wang Y X, et al. Image deformation meta-networks for one-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 8680\u20138689","DOI":"10.1109\/CVPR.2019.00888"},{"key":"3156_CR43","doi-asserted-by":"crossref","unstructured":"Chu W H, Li Y J, Chang J C, et al. Spot and learn: a maximum-entropy patch sampler for few-shot image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 6251\u20136260","DOI":"10.1109\/CVPR.2019.00641"},{"key":"3156_CR44","doi-asserted-by":"crossref","unstructured":"Bearman A, Russakovsky O, Ferrari V, et al. What\u2019s the point: semantic segmentation with point supervision. In: Proceedings of the 14th European Conference on Computer Vision, 2016. 549\u2013565","DOI":"10.1007\/978-3-319-46478-7_34"},{"key":"3156_CR45","unstructured":"Wah C, Branson S, Welinder P, et al. The caltech-ucsd birds-200-2011 dataset. 2011. https:\/\/authors.library.caltech.edu\/27452\/"},{"key":"3156_CR46","unstructured":"Hilliard N, Phillips L, Howland S, et al. Few-shot learning with metric-agnostic conditional embeddings. 2018. ArXiv:1802.04376"},{"key":"3156_CR47","doi-asserted-by":"crossref","unstructured":"Kim J, Kim T, Kim S, et al. Edge-labeling graph neural network for few-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 11\u201320","DOI":"10.1109\/CVPR.2019.00010"},{"key":"3156_CR48","unstructured":"Chen W Y, Liu Y C, Kira Z, et al. A closer look at few-shot classification. 2019. ArXiv:1904.04232"},{"key":"3156_CR49","doi-asserted-by":"crossref","unstructured":"Zhang C, Cai Y J, Lin G S, et al. DeepEMD: few-shot image classification with differentiable earth mover\u2019s distance and structured classifiers. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020. 12203\u201312213","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"3156_CR50","unstructured":"Ye H J, Hu H X, Zhan D C, et al. Learning embedding adaptation for few-shot learning. 2018. ArXiv:1812.03664"},{"key":"3156_CR51","doi-asserted-by":"crossref","unstructured":"Yang L, Li L L, Zhang Z L, et al. DPGN: distribution propagation graph network for few-shot learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2020. 13390\u201313399","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"3156_CR52","unstructured":"Schwartz E, Karlinsky L, Feris R, et al. Baby steps towards few-shot learning with multiple semantics. 2019. ArXiv: 1906.01905"},{"key":"3156_CR53","doi-asserted-by":"crossref","unstructured":"Zhang X L, Wei Y C, Feng J S, et al. Adversarial complementary learning for weakly supervised object localization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2018. 1325\u20131334","DOI":"10.1109\/CVPR.2018.00144"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-3156-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-020-3156-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-020-3156-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T21:27:28Z","timestamp":1647984448000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-020-3156-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,20]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["3156"],"URL":"https:\/\/doi.org\/10.1007\/s11432-020-3156-7","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,20]]},"assertion":[{"value":"8 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"120104"}}