{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:26Z","timestamp":1740160286188,"version":"3.37.3"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Youth Project for Basic Research of Yunnan Province Science and Technology Department","award":["202301AU070194"],"award-info":[{"award-number":["202301AU070194"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2042022kf0021"],"award-info":[{"award-number":["2042022kf0021"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Plan in Key Fields of Yunnan Province","award":["202202AD080002"],"award-info":[{"award-number":["202202AD080002"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s13042-024-02127-1","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T11:01:57Z","timestamp":1712574117000},"page":"3911-3925","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-label image classification method combining multi-stage image semantic information and label relevance"],"prefix":"10.1007","volume":"15","author":[{"given":"Liwen","family":"Wu","sequence":"first","affiliation":[]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Peigeng","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Pu","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Yudong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shaowen","family":"Yao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"key":"2127_CR1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12736","author":"S Kordnoori","year":"2023","unstructured":"Kordnoori S, Sabeti M, Mostafaei H (2023) Analysis of lung scan imaging using deep multi-task learning structure for Covid-19 disease. IET Image Process. https:\/\/doi.org\/10.1049\/ipr2.12736","journal-title":"IET Image Process"},{"key":"2127_CR2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12739","author":"K Qian","year":"2023","unstructured":"Qian K, Chen P, Zhao D (2023) GOMT: multispectral video tracking based on geneticoptimization and multi-features integration. IET Image Process. https:\/\/doi.org\/10.1049\/ipr2.12739","journal-title":"IET Image Process"},{"key":"2127_CR3","doi-asserted-by":"publisher","first-page":"140890","DOI":"10.1016\/j.cej.2022.140890","volume":"455","author":"Y Qiu","year":"2023","unstructured":"Qiu Y, Wang Z-Q, Zhu P-C (2023) A multisensory-feedback tactile glove with dense coverage of sensing arrays for object recognition. Chem Eng J 455:140890. https:\/\/doi.org\/10.1016\/j.cej.2022.140890","journal-title":"Chem Eng J"},{"key":"2127_CR4","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12743","author":"W Zhang","year":"2023","unstructured":"Zhang W, Zhang X, Tang Y (2023) Facial expression recognition based on improvedresidual network. IET Image Process. https:\/\/doi.org\/10.1049\/ipr2.12743","journal-title":"IET Image Process"},{"key":"2127_CR5","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.inffus.2023.01.016","volume":"94","author":"W-J Zhou","year":"2023","unstructured":"Zhou W-J, Yue Y-C, Fang M-X (2023) BCINet: bilateral cross-modal interaction network for indoor scene understanding in RGB-D images. Inf Fusion 94:32\u201342. https:\/\/doi.org\/10.1016\/j.inffus.2023.01.016","journal-title":"Inf Fusion"},{"key":"2127_CR6","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.neucom.2022.11.022","volume":"522","author":"J Yuan","year":"2023","unstructured":"Yuan J, Zhang Y, Shi Z-C et al (2023) Balanced masking strategy for multi-label image classification. Neurocomputing 522:64\u201372. https:\/\/doi.org\/10.1016\/j.neucom.2022.11.022","journal-title":"Neurocomputing"},{"issue":"1","key":"2127_CR7","doi-asserted-by":"publisher","first-page":"20220194","DOI":"10.1515\/nleng-2022-0194","volume":"12","author":"S-P Yuan","year":"2023","unstructured":"Yuan S-P, Chen Y, Ye C-Q et al (2023) Cross-modal multi-label image classification modeling and recognition based on nonlinear. Nonlinear Eng Model Appl 12(1):20220194. https:\/\/doi.org\/10.1515\/nleng-2022-0194","journal-title":"Nonlinear Eng Model Appl"},{"key":"2127_CR8","doi-asserted-by":"publisher","first-page":"106519","DOI":"10.1016\/j.compbiomed.2022.106519","volume":"153","author":"A Bhati","year":"2023","unstructured":"Bhati A, Gour N, Khanna P et al (2023) Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset. Comput Biol Med 153:106519. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106519","journal-title":"Comput Biol Med"},{"issue":"8","key":"2127_CR9","doi-asserted-by":"publisher","first-page":"e46","DOI":"10.1093\/nar\/gkab016","volume":"49","author":"D-L Wang","year":"2021","unstructured":"Wang D-L, Zhang Z-Y, Jiang Y-X et al (2021) DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism. Nucleic Acids Res 49(8):e46. https:\/\/doi.org\/10.1093\/nar\/gkab016","journal-title":"Nucleic Acids Res"},{"issue":"8","key":"2127_CR10","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M-L Zhang","year":"2014","unstructured":"Zhang M-L, Zhou Z-H (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819\u20131837. https:\/\/doi.org\/10.1109\/TKDE.2013.39","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"7","key":"2127_CR11","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"M-L Zhang","year":"2007","unstructured":"Zhang M-L, Zhou Z-H (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038\u20132048. https:\/\/doi.org\/10.1016\/j.patcog.2006.12.019","journal-title":"Pattern Recognit"},{"key":"2127_CR12","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neunet.2019.10.002","volume":"122","author":"G-Q Wu","year":"2020","unstructured":"Wu G-Q, Zheng R-B, Tian Y-J et al (2020) Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification. Neural Netw 122:24\u201339. https:\/\/doi.org\/10.1016\/j.neunet.2019.10.002","journal-title":"Neural Netw"},{"key":"2127_CR13","doi-asserted-by":"publisher","unstructured":"Li X, Zhao F, Guo Y et al (2014) Multi-label image classification with a probabilistic label enhancement model. In: Thirtieth conference on uncertainty in artificial intelligence. AUAI Press. https:\/\/doi.org\/10.5555\/3020751.3020796","DOI":"10.5555\/3020751.3020796"},{"key":"2127_CR14","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/978-3-642-04174-717","volume":"5782","author":"J Read","year":"2011","unstructured":"Read J, Pfahringer B, Holmes G et al (2011) Classifier chains for multi-label classification. Mach Learn 5782:333\u2013359. https:\/\/doi.org\/10.1007\/978-3-642-04174-717","journal-title":"Mach Learn"},{"key":"2127_CR15","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2127_CR16","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L et al (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition, pp 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"2127_CR17","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.1556","author":"K Simonyan","year":"2014","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","journal-title":"Comput Sci"},{"key":"2127_CR18","doi-asserted-by":"publisher","unstructured":"Li Q, Qiao M, Bian W et al (2016) Conditional graphical lasso for multi-label image classification. In: 2016 IEEE conference on computer vision and pattern recognition, pp 2977\u20132986. https:\/\/doi.org\/10.1109\/CVPR.2016.325","DOI":"10.1109\/CVPR.2016.325"},{"key":"2127_CR19","doi-asserted-by":"publisher","unstructured":"Zhu F, Li H, Wu O-Y et al (2017) Learning spatial regularization with image-level supervisions for multi-label image classification. In: 2017 IEEE conference on computer vision and pattern recognition, pp 2027\u20132036. https:\/\/doi.org\/10.1109\/CVPR.2017.219","DOI":"10.1109\/CVPR.2017.219"},{"key":"2127_CR20","doi-asserted-by":"publisher","unstructured":"Chen Z-M, Wei X-S, Wang P et al (2019) Multi-label image recognition with graph convolutional networks. In: IEEE conference on computer vision and pattern recognition, pp 5172\u20135181. https:\/\/doi.org\/10.1109\/CVPR.2019.00532","DOI":"10.1109\/CVPR.2019.00532"},{"key":"2127_CR21","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1909.13005","author":"Q Li","year":"2020","unstructured":"Li Q, Peng X, Qiao Y et al (2020) Learning category correlations for multi-label image recognition with graph networks. Pattern Recognit Lett. https:\/\/doi.org\/10.48550\/arXiv.1909.13005","journal-title":"Pattern Recognit Lett"},{"key":"2127_CR22","doi-asserted-by":"publisher","unstructured":"Guo H, Zheng K, Fan X et al (2019) Visual attention consistency under image transforms for multi-label image classification. In: 2019 IEEE conference on computer vision and pattern recognition, pp 729\u2013739. https:\/\/doi.org\/10.1109\/CVPR.2019.00082","DOI":"10.1109\/CVPR.2019.00082"},{"key":"2127_CR23","doi-asserted-by":"publisher","unstructured":"soumakas G, Vlahavas I (2007) Random K-Labelsets: an ensemble method for multilabel classification. In: Proceedings of the 18th European conference on machine learning. https:\/\/doi.org\/10.1007\/978-3-540-74958-538","DOI":"10.1007\/978-3-540-74958-538"},{"key":"2127_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009","author":"M-R Boutell","year":"2004","unstructured":"Boutell M-R, Luo J-B, Shen X-P et al (2004) Learning multi-label scene classification. Pattern Recognit. https:\/\/doi.org\/10.1016\/j.patcog.2004.03.009","journal-title":"Pattern Recognit"},{"key":"2127_CR25","doi-asserted-by":"publisher","unstructured":"Ghamrawi N, Mccallum A (2005) Collective multi-label classification. In: Proceedings of the 2005 ACM CIKM international conference on information and knowledge management. https:\/\/doi.org\/10.1145\/1099554.1099591","DOI":"10.1145\/1099554.1099591"},{"key":"2127_CR26","doi-asserted-by":"publisher","unstructured":"Lanchantin J, Wang T, Ordonez V et al (2021) General multi-label image classification with transformers. In: 2021 IEEE\/CVF conference on computer vision and pattern recognition, pp 16473\u201316483. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01621","DOI":"10.1109\/CVPR46437.2021.01621"},{"key":"2127_CR27","doi-asserted-by":"publisher","unstructured":"Yazici V-O, Gonzalez G-A, Ramisa A et al (2020) Orderless recurrent models for multi-label classification. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition, pp 13440\u201313449. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01345","DOI":"10.1109\/CVPR42600.2020.01345"},{"key":"2127_CR28","doi-asserted-by":"publisher","unstructured":"Liu S, Zhang L, Yang X, Su H, Zhu J (2021) Query2label: a simple transformer way to multi-label classification. https:\/\/doi.org\/10.48550\/arXiv.2107.10834","DOI":"10.48550\/arXiv.2107.10834"},{"key":"2127_CR29","doi-asserted-by":"publisher","unstructured":"Ridnik T, Sharir G, Ben-Cohen A, Ben-Baruch E, Noy A (2023) ML-decoder: scalable and versatile classification head. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 32\u201341. https:\/\/doi.org\/10.1109\/WACV56688.2023.00012","DOI":"10.1109\/WACV56688.2023.00012"},{"key":"2127_CR30","unstructured":"Liu R, Huang J, Li T-H, Li G (2022) Causality Compensated attention for contextual biased visual recognition. In: The eleventh international conference on learning representations"},{"key":"2127_CR31","doi-asserted-by":"publisher","unstructured":"Ye J, He J, Peng X et al (2020) Attention-driven dynamic graph convolutional network for multi-label image recognition. In: European conference on computer vision, pp 649\u2013665. https:\/\/doi.org\/10.48550\/arXiv.2012.02994","DOI":"10.48550\/arXiv.2012.02994"},{"key":"2127_CR32","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"key":"2127_CR33","doi-asserted-by":"publisher","unstructured":"Wang J, Yang Y, Mao J et al (2016) CNN-RNN: a unified framework for multi-label image classification. In: 2016 IEEE conference on computer vision and pattern recognition, pp 2285\u20132294. https:\/\/doi.org\/10.1109\/CVPR.2016.251","DOI":"10.1109\/CVPR.2016.251"},{"key":"2127_CR34","doi-asserted-by":"publisher","unstructured":"Wang Z-X, Chen T-S, Li G-B et al (2017) Multi-label image recognition by recurrently discovering attentional regions. In: International conference on computer vision, pp 464\u2013472. https:\/\/doi.org\/10.1109\/ICCV.2017.58","DOI":"10.1109\/ICCV.2017.58"},{"key":"2127_CR35","doi-asserted-by":"publisher","unstructured":"Chen S-F, Chen Y-C, Ye C-K et al (2018) Order-free RNN with visual attention for multi-label classification. In: AAAI conference on artificial intelligence, pp 66714\u20136721. https:\/\/doi.org\/10.48550\/arXiv.1707.05495","DOI":"10.48550\/arXiv.1707.05495"},{"key":"2127_CR36","doi-asserted-by":"publisher","unstructured":"Lee C-W, Fang W, Yeh C-K et al (2018) Multi-label zero-shot learning with structured knowledge graphs. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 1576\u20131585. https:\/\/doi.org\/10.1109\/CVPR.2018.00170","DOI":"10.1109\/CVPR.2018.00170"},{"key":"2127_CR37","doi-asserted-by":"publisher","unstructured":"Zhu F, Li H, W O-Y et al (2017) Learning spatial regularization with image-level supervisions for multi-label image classification. In: 2017 IEEE conference on computer vision and pattern recognition, pp 2027\u20132036. https:\/\/doi.org\/10.48550\/arXiv.1702.05891","DOI":"10.48550\/arXiv.1702.05891"},{"key":"2127_CR38","doi-asserted-by":"publisher","unstructured":"Durand T, Mehrasa N, Mori G (2019) Learning a deep ConvNet for multi-label classification with partial labels. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition, pp 647\u2013657. https:\/\/doi.org\/10.48550\/arXiv.1902.09720","DOI":"10.48550\/arXiv.1902.09720"},{"key":"2127_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3083978","author":"Z Wang","year":"2021","unstructured":"Wang Z, Fang Z, Li D et al (2021) Semantic supplementary network with prior information for multi-label image classification. IEEE Trans Circuits Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2021.3083978","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"2127_CR40","doi-asserted-by":"publisher","unstructured":"Chen T-S, Wang Z-X, Li G-b et al (2018) Recurrent attentional reinforcement learning for multi-label image recognition. In: 32th AAAI conference on artificial intelligence, pp 6730\u20136737. https:\/\/doi.org\/10.48550\/arXiv.1712.07465","DOI":"10.48550\/arXiv.1712.07465"},{"key":"2127_CR41","doi-asserted-by":"publisher","first-page":"7917","DOI":"10.1007\/s11042-021-11818-8","volume":"81","author":"L Nie","year":"2022","unstructured":"Nie L, Chen T, Wang Z et al (2022) Multi-label image recognition with attentive transformer-localizer module. Multimed Tools Appl 81:7917\u20137940. https:\/\/doi.org\/10.1007\/s11042-021-11818-8","journal-title":"Multimed Tools Appl"},{"key":"2127_CR42","doi-asserted-by":"publisher","unstructured":"Chen T, Xu M, Hui X et al (2019) Learning semantic-specific graph representation for multi-label image recognition. In: 2019 IEEE\/CVF international conference on computer vision, pp 522\u2013531. https:\/\/doi.org\/10.1109\/ICCV.2019.00061","DOI":"10.1109\/ICCV.2019.00061"},{"issue":"2","key":"2127_CR43","doi-asserted-by":"publisher","first-page":"2349","DOI":"10.1007\/s11042-022-13164-9","volume":"82","author":"Y-H Luo","year":"2023","unstructured":"Luo Y-H, Cao X, Zhang J-T et al (2023) Dynamic multi-scale loss optimization for object detection. Multimed Tools Appl 82(2):2349\u20132367. https:\/\/doi.org\/10.1007\/s11042-022-13164-9","journal-title":"Multimed Tools Appl"},{"issue":"B","key":"2127_CR44","doi-asserted-by":"publisher","first-page":"118992","DOI":"10.1016\/j.eswa.2022.118992","volume":"213","author":"P Deshmukh","year":"2023","unstructured":"Deshmukh P, Satyanarayana GSR, Majhi S et al (2023) Swin transformer based vehicle detection in undisciplined traffic environment. Expert Syst Appl 213(B):118992. https:\/\/doi.org\/10.1016\/j.eswa.2022.118992","journal-title":"Expert Syst Appl"},{"issue":"5","key":"2127_CR45","doi-asserted-by":"publisher","first-page":"6557","DOI":"10.1007\/s11042-022-13592-7","volume":"82","author":"X Wang","year":"2023","unstructured":"Wang X, Du Y-T, Verberne SZ et al (2023) Fine-grained label learning in object detection with weak supervision of captions. Multimed Tools Appl 82(5):6557\u20136579. https:\/\/doi.org\/10.1007\/s11042-022-13592-7","journal-title":"Multimed Tools Appl"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02127-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02127-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02127-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T08:24:36Z","timestamp":1723883076000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02127-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,8]]},"references-count":45,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["2127"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02127-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,4,8]]},"assertion":[{"value":"24 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This declaration is not applicable to this submission.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}