{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T06:16:28Z","timestamp":1783318588078,"version":"3.54.6"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s13042-026-03103-7","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T09:41:10Z","timestamp":1777023670000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on feature fusion-based deep adversarial hashing for cross-modal retrieval"],"prefix":"10.1007","volume":"17","author":[{"given":"Xiao fei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li ping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"3103_CR1","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1007\/s11042-019-08238-0","volume":"79","author":"D Xia","year":"2020","unstructured":"Xia D, Miao L, Fan A (2020) A cross-modal multimedia retrieval method using depth correlation mining in big data environment. Multimed Tools Appl 79:1339\u20131354. https:\/\/doi.org\/10.1007\/s11042-019-08238-0","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"3103_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447582","volume":"54","author":"P Ren","year":"2021","unstructured":"Ren P, Xiao Y, Chang X, Huang P-Y, Li Z, Chen X, Wang X (2021) A comprehensive survey of neural architecture search: challenges and solutions. ACM Comput Surv (CSUR) 54(4):1\u201334. https:\/\/doi.org\/10.1145\/3447582","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"6","key":"3103_CR3","doi-asserted-by":"publisher","first-page":"2574","DOI":"10.1109\/TKDE.2020.3015777","volume":"34","author":"M Wang","year":"2020","unstructured":"Wang M, Fu W, He X, Hao S, Wu X (2020) A survey on large-scale machine learning. IEEE Trans Knowl Data Eng 34(6):2574\u20132594. https:\/\/doi.org\/10.1109\/TKDE.2020.3015777","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"10","key":"3103_CR4","doi-asserted-by":"publisher","first-page":"4514","DOI":"10.1109\/tnnls.2020.3018790","volume":"32","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Liu L, Luo Y, Huang Z, Shen F, Shen HT, Lu G (2020) Inductive structure consistent hashing via flexible semantic calibration. IEEE Trans Neural Netw Learn Syst 32(10):4514\u20134528. https:\/\/doi.org\/10.1109\/tnnls.2020.3018790","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"3103_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3356338","volume":"15","author":"Z Ye","year":"2019","unstructured":"Ye Z, Peng Y (2019) Sequential cross-modal hashing learning via multi-scale correlation mining. ACM Trans Multimed Comput Commun Appl (TOMM) 15(4):1\u201320. https:\/\/doi.org\/10.1145\/3356338","journal-title":"ACM Trans Multimed Comput Commun Appl (TOMM)"},{"issue":"11","key":"3103_CR6","doi-asserted-by":"publisher","first-page":"3507","DOI":"10.1109\/tkde.2020.2974825","volume":"33","author":"Y Wang","year":"2020","unstructured":"Wang Y, Luo X, Nie L, Song J, Zhang W, Xu X-S (2020) Batch: a scalable asymmetric discrete cross-modal hashing. IEEE Trans Knowl Data Eng 33(11):3507\u20133519. https:\/\/doi.org\/10.1109\/tkde.2020.2974825","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3103_CR7","doi-asserted-by":"publisher","unstructured":"Su S, Zhong Z, Zhang C (2019) Deep joint-semantics reconstructing hashing for large-scale unsupervised cross-modal retrieval. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3027\u20133035. https:\/\/doi.org\/10.1109\/iccv.2019.00312","DOI":"10.1109\/iccv.2019.00312"},{"key":"3103_CR8","doi-asserted-by":"publisher","unstructured":"Huang F, Zhang L, Yang Y, Zhou X (2020) Probability weighted compact feature for domain adaptive retrieval. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9582\u20139591. https:\/\/doi.org\/10.1109\/cvpr42600.2020.00960","DOI":"10.1109\/cvpr42600.2020.00960"},{"key":"3103_CR9","doi-asserted-by":"publisher","unstructured":"Shen F, Shen C, Liu W, Tao Shen H (2015) Supervised discrete hashing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 37\u201345. https:\/\/doi.org\/10.1109\/cvpr.2015.7298598","DOI":"10.1109\/cvpr.2015.7298598"},{"issue":"9","key":"3103_CR10","doi-asserted-by":"publisher","first-page":"2827","DOI":"10.1109\/tip.2015.2421443","volume":"24","author":"J Tang","year":"2015","unstructured":"Tang J, Li Z, Wang M, Zhao R (2015) Neighborhood discriminant hashing for large-scale image retrieval. IEEE Trans Image Process 24(9):2827\u20132840. https:\/\/doi.org\/10.1109\/tip.2015.2421443","journal-title":"IEEE Trans Image Process"},{"key":"3103_CR11","doi-asserted-by":"crossref","unstructured":"Ding Y, Zhang Z, Yang A, Cai Y, Xiao X, Hong D, Yuan J (2025) SLCGC: a lightweight self-supervised low-pass contrastive graph clustering network for hyperspectral images. arXiv preprint arXiv:2502.03497","DOI":"10.1109\/TMM.2025.3604954"},{"key":"3103_CR12","first-page":"1","volume":"63","author":"Y Ding","year":"2025","unstructured":"Ding Y, Zhang Z, Kang W, Yang A, Zhao J, Feng J, Zheng Q (2025) Adaptive homophily clustering: structure homophily graph learning with adaptive filter for hyperspectral image. IEEE Trans Geosci Remote Sens 63:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"3103_CR13","doi-asserted-by":"crossref","unstructured":"Xu C, Guan Z, Zhao W, Wu H, Niu Y, Ling B (2019) Adversarial incomplete multi-view clustering. In: IJCAI, pp 933\u20133939. https:\/\/www.ijcai.org\/proceedings\/2019\/0546.pdf","DOI":"10.24963\/ijcai.2019\/546"},{"key":"3103_CR14","doi-asserted-by":"publisher","unstructured":"Xu C, Si J, Guan Z, Zhao W, Wu Y, Gao X (2024) Reliable conflictive multi-view learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 16129\u201316137. https:\/\/doi.org\/10.1609\/aaai.v38i14.29546","DOI":"10.1609\/aaai.v38i14.29546"},{"key":"3103_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3114089","author":"W Zhao","year":"2021","unstructured":"Zhao W, Xu C, Guan Z, Wu X, Zhao W, Miao Q, Wang Q (2021) TelecomNet: tag-based weakly-supervised modally cooperative hashing network for image retrieval. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2021.3114089","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3103_CR16","doi-asserted-by":"publisher","first-page":"4643","DOI":"10.1109\/tip.2020.2974065","volume":"29","author":"L Zhu","year":"2020","unstructured":"Zhu L, Lu X, Cheng Z, Li J, Zhang H (2020) Deep collaborative multi-view hashing for large-scale image search. IEEE Trans Image Process 29:4643\u20134655. https:\/\/doi.org\/10.1109\/tip.2020.2974065","journal-title":"IEEE Trans Image Process"},{"key":"3103_CR17","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1117\/12.647755","volume-title":"Proceedings Volume 6073, Multimedia Content Analysis, Management, and Retrieval 2006","author":"J S \/ P H \/ P G \/ C J Hare \/ Lewis \/ Enser \/ Sandom","year":"2006","unstructured":"Hare JS, Lewis PH, Enser PG, Sandom CJ (2006) Mind the gap: Another look at the problem of the semantic gap in image retrieval. Proceedings Volume 6073, Multimedia Content Analysis, Management, and Retrieval 2006, 607309. SPIE. https:\/\/doi.org\/10.1117\/12.647755"},{"key":"3103_CR18","doi-asserted-by":"publisher","first-page":"9530","DOI":"10.1109\/tmm.2023.3254199","volume":"25","author":"X Liu","year":"2023","unstructured":"Liu X, Zeng H, Shi Y, Zhu J, Hsia C-H, Ma K-K (2023) Deep cross-modal hashing based on semantic consistent ranking. IEEE Trans Multimed 25:9530\u20139542. https:\/\/doi.org\/10.1109\/tmm.2023.3254199","journal-title":"IEEE Trans Multimed"},{"key":"3103_CR19","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.sigpro.2018.09.007","volume":"154","author":"X Lu","year":"2019","unstructured":"Lu X, Zhu L, Cheng Z, Song X, Zhang H (2019) Efficient discrete latent semantic hashing for scalable cross-modal retrieval. Signal Process 154:217\u2013231. https:\/\/doi.org\/10.1016\/j.sigpro.2018.09.007","journal-title":"Signal Process"},{"key":"3103_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108823","volume":"130","author":"F Yang","year":"2022","unstructured":"Yang F, Liu Y, Ding X, Ma F, Cao J (2022) Asymmetric cross-modal hashing with high-level semantic similarity. Pattern Recogn 130:108823. https:\/\/doi.org\/10.1016\/j.patcog.2022.108823","journal-title":"Pattern Recogn"},{"issue":"10","key":"3103_CR21","doi-asserted-by":"publisher","first-page":"10064","DOI":"10.1109\/TCYB.2021.3059886","volume":"52","author":"Y Wang","year":"2021","unstructured":"Wang Y, Chen Z-D, Luo X, Li R, Xu X-S (2021) Fast crossmodal hashing with global and local similarity embedding. IEEE Trans Cybern 52(10):10064\u201310077. https:\/\/doi.org\/10.1109\/TCYB.2021.3059886","journal-title":"IEEE Trans Cybern"},{"key":"3103_CR22","doi-asserted-by":"publisher","unstructured":"Huang P-Y, Kang G, Liu W, Chang X, Hauptmann AG (2019) Annotation efficient cross-modal retrieval with adversarial attentive alignment. In: Proceedings of the 27th ACM international conference on multimedia, pp 1758\u20131767. https:\/\/doi.org\/10.1145\/3343031.3350894","DOI":"10.1145\/3343031.3350894"},{"key":"3103_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100336","volume":"39","author":"P Kaur","year":"2021","unstructured":"Kaur P, Pannu HS, Malhi AK (2021) Comparative analysis on cross-modal information retrieval: a review. Comput Sci Rev 39:100336. https:\/\/doi.org\/10.1016\/j.cosrev.2020.100336","journal-title":"Comput Sci Rev"},{"key":"3103_CR24","doi-asserted-by":"publisher","unstructured":"Yang D, Wu D, Zhang W, Zhang H, Li B, Wang W (2020) Deep semantic alignment hashing for unsupervised cross-modal retrieval. In: Proceedings of the 2020 international conference on multimedia retrieval, pp 44\u201352. https:\/\/doi.org\/10.1145\/3372278.3390673","DOI":"10.1145\/3372278.3390673"},{"key":"3103_CR25","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1109\/TMM.2021.3053766","volume":"24","author":"PF Zhang","year":"2022","unstructured":"Zhang PF, Li Y, Huang Z, Xu XS (2022) Aggregation-based graph convolutional hashing for unsupervised cross-modal retrieval. IEEE Trans Multimed 24:466\u2013479. https:\/\/doi.org\/10.1109\/TMM.2021.3053766","journal-title":"IEEE Trans Multimed"},{"key":"3103_CR26","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s11760-019-01534-0","volume":"15","author":"Y Li","year":"2021","unstructured":"Li Y, Wang X, Qi S, Huang C, Jiang ZL, Liao Q, Guan J, Zhang J (2021) Self-supervised learning-based weight adaptive hashing for fast cross-modal retrieval. Signal Image Video Process 15:673\u2013680. https:\/\/doi.org\/10.1007\/s11760-019-01534-0","journal-title":"Signal Image Video Process"},{"key":"3103_CR27","doi-asserted-by":"publisher","unstructured":"Jiang, QY, Li WJ (2017) Deep cross-modal hashing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3232\u20133240. https:\/\/doi.org\/10.1109\/cvpr.2017.348","DOI":"10.1109\/cvpr.2017.348"},{"key":"3103_CR28","doi-asserted-by":"publisher","unstructured":"Li C, Deng C, Li N, Liu W, Gao X, Tao D (2018) Self-supervised adversarial hashing networks for cross-modal retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/cvpr.2018.00446","DOI":"10.1109\/cvpr.2018.00446"},{"key":"3103_CR29","doi-asserted-by":"publisher","unstructured":"Gu W, Gu X, Gu J, Li B, Xiong Z, Wang W (2019) Adversary guided asymmetric hashing for cross-modal retrieval. In: Proceedings of the 2019 on international conference on multimedia retrieval, pp 159\u2013167. https:\/\/doi.org\/10.1145\/3323873.3325045","DOI":"10.1145\/3323873.3325045"},{"issue":"12","key":"3103_CR30","doi-asserted-by":"publisher","first-page":"3101","DOI":"10.1109\/tmm.2020.2969792","volume":"22","author":"X Ma","year":"2020","unstructured":"Ma X, Zhang T, Xu C (2020) Multi-level correlation adversarial hashing for crossmodal retrieval. IEEE Trans Multimed 22(12):3101\u20133114. https:\/\/doi.org\/10.1109\/tmm.2020.2969792","journal-title":"IEEE Trans Multimed"},{"key":"3103_CR31","doi-asserted-by":"publisher","unstructured":"Song J, Yang Y, Yang Y, Huang Z, Shen HT (2013) Inter-media hashing for large-scale retrieval from heterogeneous data sources. In: Proceedings of the 2013 ACM SIGMOD international conference on management of data, pp 785\u2013796. https:\/\/doi.org\/10.1145\/2463676.2465274","DOI":"10.1145\/2463676.2465274"},{"key":"3103_CR32","unstructured":"Andrew G, Arora R, Bilmes J, Livescu K (2013) Deep canonical correlation analysis. In: International conference on international conference on machine learning, PMLR, vol 28, no 3, pp 1247\u20131255"},{"key":"3103_CR33","doi-asserted-by":"publisher","unstructured":"Ranjan V, Rasiwasia N, Jawahar CV (2015) Multi-label cross-modal retrieval. In: Proceedings of the IEEE international conference on computer vision, pp 4094\u20134102. https:\/\/doi.org\/10.1109\/iccv.2015.466","DOI":"10.1109\/iccv.2015.466"},{"key":"3103_CR34","doi-asserted-by":"publisher","unstructured":"Tran, TQN, Le Borgne H, Crucianu M (2016) Aggregating image and text quantized correlated components. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2046\u20132054. https:\/\/doi.org\/10.1109\/cvpr.2016.225","DOI":"10.1109\/cvpr.2016.225"},{"issue":"11","key":"3103_CR35","doi-asserted-by":"publisher","first-page":"5585","DOI":"10.1109\/tip.2018.2852503","volume":"27","author":"Y Peng","year":"2018","unstructured":"Peng Y, Qi J, Yuan Y (2018) Modality-specific cross-modal similarity measurement with recurrent attention network. IEEE Trans Image Process 27(11):5585\u20135599. https:\/\/doi.org\/10.1109\/tip.2018.2852503","journal-title":"IEEE Trans Image Process"},{"key":"3103_CR36","doi-asserted-by":"crossref","unstructured":"Cao Y, Liu B, Long M, Wang J (2018) Cross-modal hamming hashing. In: Proceedings of the European conference on computer vision (ECCV), pp 202\u2013218","DOI":"10.1007\/978-3-030-01246-5_13"},{"key":"3103_CR37","doi-asserted-by":"publisher","unstructured":"Tu J, Liu X, Lin Z, Hong R, Wang M (2022) Differentiable cross-modal hashing via multimodal transformers. In: Proceedings of the 30th ACM international conference on multimedia, pp 453\u2013461. https:\/\/doi.org\/10.1145\/3503161.3548187","DOI":"10.1145\/3503161.3548187"},{"issue":"1","key":"3103_CR38","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1109\/TCSVT.2023.3285266","volume":"34","author":"Y Huo","year":"2024","unstructured":"Huo Y, Qin Q, Dai J, Wang L, Zhang W, Huang L, Wang C (2024) Deep semantic-aware proxy hashing for multi-label cross-modal retrieval. IEEE Trans Circuits Syst Video Technol 34(1):576\u2013589. https:\/\/doi.org\/10.1109\/TCSVT.2023.3285266","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"3103_CR39","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.knosys.2019.05.017","volume":"180","author":"P Hu","year":"2019","unstructured":"Hu P, Peng D, Wang X, Xiang Y (2019) Multimodal adversarial network for cross-modal retrieval. Knowl-Based Syst 180:38\u201350. https:\/\/doi.org\/10.1016\/j.knosys.2019.05.017","journal-title":"Knowl-Based Syst"},{"key":"3103_CR40","doi-asserted-by":"publisher","unstructured":"Wang B, Yang Y, Xu X, Hanjalic A, Shen HT (2017) Adversarial cross-modal retrieval. In: Proceedings of the 25th ACM international conference on multimedia, pp 154\u2013162. https:\/\/doi.org\/10.1145\/3123266.3123326","DOI":"10.1145\/3123266.3123326"},{"key":"3103_CR41","doi-asserted-by":"publisher","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative Adversarial Nets. In Advances in Neural Information Processing Systems, vol 27, pp 2672\u20132680. MIT Press. https:\/\/doi.org\/10.48550\/arXiv.1406.2661","DOI":"10.48550\/arXiv.1406.2661"},{"issue":"1","key":"3103_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3284750","volume":"15","author":"Y Peng","year":"2019","unstructured":"Peng Y, Qi J (2019) CM-GANs: cross-modal generative adversarial networks for common representation learning. ACM Trans Multimed Comput Commun Appl (TOMM) 15(1):1\u201324. https:\/\/doi.org\/10.1145\/3284750","journal-title":"ACM Trans Multimed Comput Commun Appl (TOMM)"},{"key":"3103_CR43","doi-asserted-by":"publisher","unstructured":"Bai C, Zeng C, Ma Q, Zhang J, Chen S (2020) Deep adversarial discrete hashing for cross-modal retrieval. In: Proceedings of the 2020 international conference on multimedia retrieval, pp 525\u2013531. https:\/\/doi.org\/10.1145\/3372278.3390711","DOI":"10.1145\/3372278.3390711"},{"key":"3103_CR44","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J (2021) Learning transferable visual models from natural language supervision. In: International conference on machine learning, vol 139. PMLR, pp 8748\u20138763"},{"key":"3103_CR45","doi-asserted-by":"publisher","first-page":"35544","DOI":"10.52202\/075280-1544","volume":"36","author":"L Fan","year":"2023","unstructured":"Fan L, Krishnan D, Isola P, Katabi D, Tian Y (2023) Improving clip training with language rewrites. Adv Neural Inf Process Syst 36:35544\u201335575","journal-title":"Adv Neural Inf Process Syst"},{"key":"3103_CR46","unstructured":"Zeng Z, Mao W (2022) A comprehensive empirical study of vision language pre-trained model for supervised cross-modal retrieval. arxiv preprint arxiv:2201.02772"},{"key":"3103_CR47","doi-asserted-by":"crossref","unstructured":"Yang C, An Z, Huang L, Bi J, Yu X, Yang H, Xu Y (2024) CLIP-KD: an empirical study of clip model distillation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15952\u201315962","DOI":"10.1109\/CVPR52733.2024.01510"},{"key":"3103_CR48","doi-asserted-by":"publisher","unstructured":"Huang W, Wu A, Yang Y, Luo X, Yang Y, Hu L, Dai Q, Dai X, Chen D, Luo C, Qiu L (2024) LLM2CLIP: powerful language model unlocks richer visual representation. https:\/\/doi.org\/10.48550\/arXiv.2411.04997","DOI":"10.48550\/arXiv.2411.04997"},{"key":"3103_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101968","volume":"100","author":"X Xia","year":"2023","unstructured":"Xia X, Dong G, Li F, Zhu L, Ying X (2023) When clip meets cross-modal hashing retrieval: a new strong baseline. Inf Fusion 100:101968. https:\/\/doi.org\/10.1016\/j.inffus.2023.101968","journal-title":"Inf Fusion"},{"key":"3103_CR50","doi-asserted-by":"publisher","unstructured":"Vaswani A (2017) Attention is all you need. In: Advances in neural information processing systems. https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"3103_CR51","doi-asserted-by":"crossref","unstructured":"Jiang D, Ye M (2023) Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 2787\u20132797","DOI":"10.1109\/CVPR52729.2023.00273"},{"issue":"6","key":"3103_CR52","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1080\/00207721.2016.1255803","volume":"48","author":"Y Xu","year":"2017","unstructured":"Xu Y, Rui D, Wang H (2017) A dynamically weight adjustment in the consensus reaching process for group decision-making with hesitant fuzzy preference relations. Int J Syst Sci 48(6):1311\u20131321. https:\/\/doi.org\/10.1080\/00207721.2016.1255803","journal-title":"Int J Syst Sci"},{"issue":"5555\/3454287","key":"3103_CR53","first-page":"3454404","volume":"10","author":"B Yang","year":"2019","unstructured":"Yang B, Bender G, Le QV, Ngiam J (2019) CondConv: conditionally parameterized convolutions for efficient inference. Adv Neural Inf Process Syst 10(5555\/3454287):3454404","journal-title":"Adv Neural Inf Process Syst"},{"key":"3103_CR54","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.neucom.2020.03.019","volume":"400","author":"X Wang","year":"2020","unstructured":"Wang X, Zou X, Bakker EM, Wu S (2020) Self-constraining and attention-based hashing network for bit-scalable cross-modal retrieval. Neurocomputing 400:255\u2013271. https:\/\/doi.org\/10.1016\/j.neucom.2020.03.019","journal-title":"Neurocomputing"},{"key":"3103_CR55","doi-asserted-by":"publisher","unstructured":"Huiskes MJ, Lew, MS (2008) The MIR flickr retrieval evaluation. In: Proceedings of the 1st ACM international conference on multimedia information retrieval, pp 39\u201343. https:\/\/doi.org\/10.1145\/1460096.1460104","DOI":"10.1145\/1460096.1460104"},{"key":"3103_CR56","doi-asserted-by":"publisher","unstructured":"Chua TS, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) NUS-WIDE: a real-world web image database from national university of Singapore. In: Proceedings of the ACM international conference on image and video retrieval, pp 1\u20139. https:\/\/doi.org\/10.1145\/1646396.1646452","DOI":"10.1145\/1646396.1646452"},{"key":"3103_CR57","doi-asserted-by":"publisher","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft COCO: common objects in context. In: Computer Vision\u2014ECCV 2014. Springer International Publishing, pp 740\u2013755. https:\/\/doi.org\/10.1007\/978-3-319-10602-148","DOI":"10.1007\/978-3-319-10602-148"},{"key":"3103_CR58","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) GloVe: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03103-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03103-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03103-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:56:04Z","timestamp":1783317364000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03103-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":58,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["3103"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03103-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"11 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"278"}}