{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T16:16:48Z","timestamp":1726503408020},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013093","name":"Science and Technology Planning Project of Shenzhen Municipality","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1016\/j.patcog.2021.107946","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T22:04:45Z","timestamp":1619129085000},"page":"107946","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":19,"special_numbering":"C","title":["Learn to abstract via concept graph for weakly-supervised few-shot learning"],"prefix":"10.1016","volume":"117","author":[{"given":"Baoquan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Ka-Cheong","family":"Leung","sequence":"additional","affiliation":[]},{"given":"Xutao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yunming","family":"Ye","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2021.107946_bib0001","doi-asserted-by":"crossref","first-page":"107160","DOI":"10.1016\/j.patcog.2019.107160","article-title":"Few-shot traffic sign recognition with clustering inductive bias and random neural network","volume":"100","author":"Zhou","year":"2020","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.patcog.2021.107946_bib0002","series-title":"Advances in Neural Information Processing Systems","first-page":"4077","article-title":"Prototypical networks for few-shot learning","author":"Snell","year":"2017"},{"key":"10.1016\/j.patcog.2021.107946_bib0003","doi-asserted-by":"crossref","first-page":"77597","DOI":"10.1109\/ACCESS.2019.2922438","article-title":"Meta-SSD: towards fast adaptation for few-shot object detection with meta-learning","volume":"7","author":"Fu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.patcog.2021.107946_bib0004","series-title":"Proceedings of the IEEE International Conference on Computer Vision","article-title":"Metapruning: meta learning for automatic neural network channel pruning","author":"Liu","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0005","series-title":"Advances in Neural Information Processing Systems","first-page":"6904","article-title":"A meta-learning perspective on cold-start recommendations for items","author":"Vartak","year":"2017"},{"issue":"4","key":"10.1016\/j.patcog.2021.107946_bib0006","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1021\/acscentsci.6b00367","article-title":"Low data drug discovery with one-shot learning","volume":"3","author":"Altae-Tran","year":"2017","journal-title":"ACS Cent. Sci."},{"key":"10.1016\/j.patcog.2021.107946_bib0007","series-title":"Proceedings of the 34th International Conference on Machine Learning","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"Finn","year":"2017"},{"key":"10.1016\/j.patcog.2021.107946_bib0008","first-page":"44","article-title":"A brief introduction to weakly supervised learning","volume":"5-1","author":"H","year":"2017","journal-title":"Natl. Sci. Rev."},{"key":"10.1016\/j.patcog.2021.107946_bib0009","series-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","first-page":"11487","article-title":"Rethinking knowledge graph propagation for zero-shot learning","author":"Kampffmeyer","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0010","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2219","article-title":"Attention-based dropout layer for weakly supervised object localization","author":"Choe","year":"2019"},{"issue":"3","key":"10.1016\/j.patcog.2021.107946_bib0011","doi-asserted-by":"crossref","first-page":"70:1","DOI":"10.1145\/3209666","article-title":"User-click-data-based fine-grained image recognition via weakly supervised metric learning","volume":"14","author":"Tan","year":"2018","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.patcog.2021.107946_bib0012","series-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","first-page":"3015","article-title":"Prototype propagation networks (PPN) for weakly-supervised few-shot learning on category graph","author":"Liu","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0013","series-title":"Advances in neural information processing systems","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"Mikolov","year":"2013"},{"key":"10.1016\/j.patcog.2021.107946_bib0014","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","article-title":"Enriching word vectors with subword information","volume":"5","author":"Bojanowski","year":"2017","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"10.1016\/j.patcog.2021.107946_bib0015","series-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing","first-page":"1532","article-title":"Glove: global vectors for word representation","author":"Pennington","year":"2014"},{"key":"10.1016\/j.patcog.2021.107946_bib0016","unstructured":"J. Devlin, M. Chang, K. Lee, K. Toutanova, BERT: pre-training of deep bidirectional transformers for language understanding, in: J. Burstein, C. Doran, T. Solorio (Eds.), NAACL-HLT, pp. 4171\u20134186."},{"key":"10.1016\/j.patcog.2021.107946_bib0017","doi-asserted-by":"crossref","first-page":"106962","DOI":"10.1016\/j.patcog.2019.07.007","article-title":"Scheduled sampling for one-shot learning via matching network","volume":"96","author":"Zhang","year":"2019","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2021.107946_bib0018","series-title":"International Conference on Learning Representations","article-title":"A closer look at few-shot classification","author":"Chen","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0019","series-title":"Advances in Neural Information Processing Systems","first-page":"3630","article-title":"Matching networks for one shot learning","author":"Vinyals","year":"2016"},{"issue":"2","key":"10.1016\/j.patcog.2021.107946_bib0020","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TNNLS.2020.2979745","article-title":"Hypersphere-based weight imprinting for few-shot learning on embedded devices","volume":"32","author":"Passalis","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2021.107946_bib0021","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"11","article-title":"Edge-labeling graph neural network for few-shot learning","author":"Kim","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0022","series-title":"International Conference on Learning Representations","article-title":"Learning to propagate labels: transductive propagation network for few-shot learning","author":"Liu","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0023","series-title":"International Conference on Learning Representations","article-title":"Few-shot learning with graph neural networks","author":"Satorras","year":"2018"},{"key":"10.1016\/j.patcog.2021.107946_bib0024","series-title":"Advances in Neural Information Processing Systems","first-page":"4848","article-title":"Adaptive cross-modal few-shot learning","author":"Xing","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0025","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7212","article-title":"Large-scale few-shot learning: knowledge transfer with class hierarchy","author":"Li","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0026","series-title":"International Conference on Learning Representations","article-title":"Meta-learning with latent embedding optimization","author":"Rusu","year":"2018"},{"key":"10.1016\/j.patcog.2021.107946_bib0027","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"10657","article-title":"Meta-learning with differentiable convex optimization","author":"Lee","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0028","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","first-page":"11","article-title":"Edge-labeling graph neural network for few-shot learning","author":"Kim","year":"2019"},{"key":"10.1016\/j.patcog.2021.107946_bib0029","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13387","article-title":"DPGN: distribution propagation graph network for few-shot learning","author":"Yang","year":"2020"},{"key":"10.1016\/j.patcog.2021.107946_bib0030","series-title":"Computer Vision - ECCV 2020 - 16th European Conference","first-page":"121","article-title":"Embedding propagation: smoother manifold for few-shot classification","volume":"12371","author":"Rodr\u00edguez","year":"2020"},{"issue":"9","key":"10.1016\/j.patcog.2021.107946_bib0031","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","article-title":"Zero-shot learning\u2014A comprehensive evaluation of the good, the bad and the ugly","volume":"41","author":"Xian","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2021.107946_bib0032","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.patcog.2018.12.010","article-title":"Zero-shot event detection via event-adaptive concept relevance mining","volume":"88","author":"Li","year":"2019","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.patcog.2021.107946_bib0033","doi-asserted-by":"crossref","first-page":"107370","DOI":"10.1016\/j.patcog.2020.107370","article-title":"Deep transductive network for generalized zero shot learning","volume":"105","author":"Zhang","year":"2020","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.patcog.2021.107946_bib0034","series-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","first-page":"6857","article-title":"Zero-shot recognition via semantic embeddings and knowledge graphs","author":"Wang","year":"2018"},{"key":"10.1016\/j.patcog.2021.107946_bib0035","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"441","article-title":"Few-shot image recognition with knowledge transfer","author":"Peng","year":"2019"},{"issue":"1","key":"10.1016\/j.patcog.2021.107946_bib0036","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2021.107946_bib0037","series-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","first-page":"349","article-title":"Cross-lingual knowledge graph alignment via graph convolutional networks","author":"Wang","year":"2018"},{"key":"10.1016\/j.patcog.2021.107946_bib0038","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"401","article-title":"Learning human-object interactions by graph parsing neural networks","author":"Qi","year":"2018"},{"key":"10.1016\/j.patcog.2021.107946_bib0039","doi-asserted-by":"crossref","first-page":"107321","DOI":"10.1016\/j.patcog.2020.107321","article-title":"Graph convolutional network with structure pooling and joint-wise channel attention for action recognition","volume":"103","author":"Chen","year":"2020","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.patcog.2021.107946_bib0040","series-title":"International Conference on Learning Representations","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.patcog.2021.107946_bib0041","series-title":"Proceedings of the 24th ACM International Conference on Information and Knowledge Management","first-page":"653","article-title":"An inference approach to basic level of categorization","author":"Wang","year":"2015"},{"issue":"11","key":"10.1016\/j.patcog.2021.107946_bib0042","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"Wordnet: a lexical database for english","volume":"38","author":"Miller","year":"1995","journal-title":"Commun. ACM"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320321001333?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320321001333?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T10:20:12Z","timestamp":1672395612000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320321001333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9]]},"references-count":42,"alternative-id":["S0031320321001333"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2021.107946","relation":{},"ISSN":["0031-3203"],"issn-type":[{"type":"print","value":"0031-3203"}],"subject":[],"published":{"date-parts":[[2021,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Learn to abstract via concept graph for weakly-supervised few-shot learning","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2021.107946","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107946"}}