{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:22:12Z","timestamp":1779175332840,"version":"3.51.4"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010905","name":"Major Research Plan","doi-asserted-by":"publisher","award":["61976132"],"award-info":[{"award-number":["61976132"]}],"id":[{"id":"10.13039\/501100010905","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010901","name":"Joint Fund of Research utilizing Large-scale Scientific Facilities","doi-asserted-by":"publisher","award":["U1811461"],"award-info":[{"award-number":["U1811461"]}],"id":[{"id":"10.13039\/501100010901","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["19ZR1419200"],"award-info":[{"award-number":["19ZR1419200"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010903","name":"Key Programme","doi-asserted-by":"publisher","award":["61991411"],"award-info":[{"award-number":["61991411"]}],"id":[{"id":"10.13039\/501100010903","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10489-022-03649-3","type":"journal-article","created":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T22:02:35Z","timestamp":1654725755000},"page":"4380-4392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adversarial multi-task deep learning for signer-independent feature representation"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7085-8876","authenticated-orcid":false,"given":"Yuchun","family":"Fang","sequence":"first","affiliation":[]},{"given":"Zhengye","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Sirui","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Lan","family":"Ni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"issue":"11","key":"3649_CR1","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1109\/TMM.2015.2477680","volume":"17","author":"AH Abdulnabi","year":"2015","unstructured":"Abdulnabi AH, Wang G, Lu J, Jia K (2015) Multi-task cnn model for attribute prediction. IEEE Trans Multimed 17(11):1949\u2013 1959","journal-title":"IEEE Trans Multimed"},{"key":"3649_CR2","unstructured":"Adaloglou N, Chatzis T, Papastratis I, Stergioulas A, Papadopoulos GT, Zacharopoulou V, Xydopoulos GJ, Atzakas K, Papazachariou D, Daras P (2020) A comprehensive study on sign language recognition methods. arXiv:2007.12530"},{"key":"3649_CR3","doi-asserted-by":"crossref","unstructured":"Adaloglou NM, Chatzis T, Papastratis I, Stergioulas A, Papadopoulos GT, Zacharopoulou V, Xydopoulos G, Antzakas K, Papazachariou D, none Daras P (2021) A comprehensive study on deep learning-based methods for sign language recognition. IEEE Trans Multimed","DOI":"10.1109\/TMM.2021.3070438"},{"key":"3649_CR4","doi-asserted-by":"crossref","unstructured":"Adi Y, Zeghidour N, Collobert R, Usunier N, Liptchinsky V, Synnaeve G (2019) To reverse the gradient or not: an empirical comparison of adversarial and multi-task learning in speech recognition. In: ICASSP 2019-2019 IEEE International conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 3742\u20133746","DOI":"10.1109\/ICASSP.2019.8682468"},{"issue":"1","key":"3649_CR5","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2019","unstructured":"Cao Z, Hidalgo G, Simon T, Wei SE, Sheikh Y (2019) Openpose: realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell 43(1):172\u2013186","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3649_CR6","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"issue":"1","key":"3649_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Mach Learn 28(1):41\u201375","journal-title":"Mach Learn"},{"key":"3649_CR8","doi-asserted-by":"crossref","unstructured":"Cui F, Di H, Shen L, Ouchi K, Liu Z, Xu J (2021) Modeling semantic and emotional relationship in multi-turn emotional conversations using multi-task learning. Appl Intell, pp 1\u201311","DOI":"10.1007\/s10489-021-02683-x"},{"issue":"7","key":"3649_CR9","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.1109\/TMM.2018.2889563","volume":"21","author":"R Cui","year":"2019","unstructured":"Cui R, Liu H, Zhang C (2019) A deep neural framework for continuous sign language recognition by iterative training. IEEE Trans Multimed 21(7):1880\u20131891","journal-title":"IEEE Trans Multimed"},{"key":"3649_CR10","doi-asserted-by":"crossref","unstructured":"Deng J, Cheng S, Xue N, Zhou Y, Zafeiriou S (2018) Uv-gan: Adversarial facial uv map completion for pose-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7093\u20137102","DOI":"10.1109\/CVPR.2018.00741"},{"key":"3649_CR11","doi-asserted-by":"crossref","unstructured":"Du L, Ling H (2014) Exploiting competition relationship for robust visual recognition. In: Twenty-eighth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v28i1.9137"},{"key":"3649_CR12","doi-asserted-by":"crossref","unstructured":"Escalera S, Bar\u00f3 X., Gonzalez J, Bautista MA, Madadi M, Reyes M, Ponce-L\u00f3pez V, Escalante HJ, Shotton J, Guyon I (2014) Chalearn looking at people challenge 2014: Dataset and results. In: European conference on computer vision, Springer, pp 459\u2013 473","DOI":"10.1007\/978-3-319-16178-5_32"},{"key":"3649_CR13","doi-asserted-by":"crossref","unstructured":"Fang Y, Ma Z, Zhang Z, Zhang XY, Bai X, et al. (2017) Dynamic multi-task learning with convolutional neural network. In: IJCAI, pp 1668\u20131674","DOI":"10.24963\/ijcai.2017\/231"},{"key":"3649_CR14","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.neucom.2021.07.068","volume":"463","author":"Y Fang","year":"2021","unstructured":"Fang Y, Xiao Z, Zhang W (2021) Multi-layer adversarial domain adaptation with feature joint distribution constraint. Neurocomputing 463:298\u2013308","journal-title":"Neurocomputing"},{"key":"3649_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2015.02.008","volume":"134","author":"D Fortun","year":"2015","unstructured":"Fortun D, Bouthemy P, Kervrann C (2015) Optical flow modeling and computation: a survey. Comput Vis Image Underst 134:1\u201321","journal-title":"Comput Vis Image Underst"},{"issue":"1","key":"3649_CR16","first-page":"189","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):189\u2013209","journal-title":"J Mach Learn Res"},{"key":"3649_CR17","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, pp 2672\u20132680"},{"issue":"1","key":"3649_CR18","first-page":"1","volume":"14","author":"D Guo","year":"2017","unstructured":"Guo D, Zhou W, Li H, Wang M (2017) Online early-late fusion based on adaptive hmm for sign language recognition. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14(1):1\u201318","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)"},{"key":"3649_CR19","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y (2018) Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp 6546\u20136555","DOI":"10.1109\/CVPR.2018.00685"},{"key":"3649_CR20","doi-asserted-by":"crossref","unstructured":"He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5353\u20135360","DOI":"10.1109\/CVPR.2015.7299173"},{"issue":"9","key":"3649_CR21","doi-asserted-by":"publisher","first-page":"2822","DOI":"10.1109\/TCSVT.2018.2870740","volume":"29","author":"J Huang","year":"2018","unstructured":"Huang J, Zhou W, Li H, Li W (2018) Attention-based 3d-cnns for large-vocabulary sign language recognition. IEEE Trans Circuits Syst Video Technol 29(9):2822\u20132832","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"1","key":"3649_CR22","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2012","unstructured":"Ji S, Xu W, Yang M (2012) Yu, k.: 3d convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221\u2013231","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3649_CR23","doi-asserted-by":"crossref","unstructured":"Jiang B, Zhou Z, Wang X, Tang J, Luo B (2020) cmsalgan: Rgb-d salient object detection with cross-view generative adversarial networks. IEEE Transactions on Multimedia","DOI":"10.1109\/TMM.2020.2997184"},{"key":"3649_CR24","doi-asserted-by":"crossref","unstructured":"Koller O, Camgoz C, Ney H, Bowden R (2019) Weakly supervised learning with multi-stream cnn-lstm-hmms to discover sequential parallelism in sign language videos. IEEE transactions on pattern analysis and machine intelligence","DOI":"10.1109\/TPAMI.2019.2911077"},{"issue":"2-3","key":"3649_CR25","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11263-005-1838-7","volume":"64","author":"I Laptev","year":"2005","unstructured":"Laptev I (2005) On space-time interest points. International Journal of Computer Vision 64 (2-3):107\u2013123","journal-title":"International Journal of Computer Vision"},{"key":"3649_CR26","doi-asserted-by":"crossref","unstructured":"Li Y, Ji B, Shi X, Zhang J, Kang B, Wang L (2020) Tea: Temporal excitation and aggregation for action recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 909\u2013918","DOI":"10.1109\/CVPR42600.2020.00099"},{"key":"3649_CR27","doi-asserted-by":"crossref","unstructured":"Liu H, Sun P, Zhang J, Wu S, Yu Z, Sun X (2020) Similarity-aware and variational deep adversarial learning for robust facial age estimation. IEEE Trans Multimed","DOI":"10.1109\/TMM.2020.2969793"},{"key":"3649_CR28","doi-asserted-by":"crossref","unstructured":"Liu Y, Wei F, Shao J, Sheng L, Yan J, Wang X (2018) Exploring disentangled feature representation beyond face identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2080\u20132089","DOI":"10.1109\/CVPR.2018.00222"},{"key":"3649_CR29","doi-asserted-by":"crossref","unstructured":"Meng Z, Li J, Chen Z, Zhao Y, Mazalov V, Gang Y, Juang BH (2018) Speaker-invariant training via adversarial learning. In: 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 5969\u20135973","DOI":"10.1109\/ICASSP.2018.8461932"},{"key":"3649_CR30","doi-asserted-by":"crossref","unstructured":"Mullick K, Namboodiri AM (2017) Learning deep and compact models for gesture recognition. In: 2017 IEEE International conference on image processing (ICIP), IEEE, pp 3998\u20134002","DOI":"10.1109\/ICIP.2017.8297033"},{"key":"3649_CR31","doi-asserted-by":"crossref","unstructured":"Pu J, Zhou W, Li H (2019) Iterative alignment network for continuous sign language recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4165\u20134174","DOI":"10.1109\/CVPR.2019.00429"},{"key":"3649_CR32","doi-asserted-by":"crossref","unstructured":"Qiu Z, Yao T, Mei T (2017) Learning spatio-temporal representation with pseudo-3d residual networks. In: Proceedings of the IEEE international conference on computer vision, pp 5533\u20135541","DOI":"10.1109\/ICCV.2017.590"},{"issue":"113","key":"3649_CR33","first-page":"794","volume":"164","author":"R Rastgoo","year":"2021","unstructured":"Rastgoo R, Kiani K, Escalera S (2021) Sign language recognition: a deep survey. Expert Syst Appl 164(113):794","journal-title":"Expert Syst Appl"},{"key":"3649_CR34","unstructured":"Romera-Paredes B, Argyriou A, Berthouze N, Pontil M (2012) Exploiting unrelated tasks in multi-task learning. In: International conference on artificial intelligence and statistics, pp 951\u2013959"},{"key":"3649_CR35","doi-asserted-by":"crossref","unstructured":"Shinohara Y (2016) Adversarial multi-task learning of deep neural networks for robust speech recognition. In: Interspeech, San Francisco, CA, USA, pp 2369\u20132372","DOI":"10.21437\/Interspeech.2016-879"},{"key":"3649_CR36","doi-asserted-by":"crossref","unstructured":"Si C, Nie X, Wang W, Wang L, Tan T, Feng J (2020) Adversarial self-supervised learning for semi-supervised 3d action recognition. In: European conference on computer vision, Springer, pp 35\u201351","DOI":"10.1007\/978-3-030-58571-6_3"},{"key":"3649_CR37","doi-asserted-by":"crossref","unstructured":"Song L, Zhang M, Wu X, He R (2018) Adversarial discriminative heterogeneous face recognition. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.12291"},{"key":"3649_CR38","doi-asserted-by":"crossref","unstructured":"Sudhakaran S, Escalera S, Lanz O (2020) Gate-shift networks for video action recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1102\u20131111","DOI":"10.1109\/CVPR42600.2020.00118"},{"key":"3649_CR39","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"key":"3649_CR40","doi-asserted-by":"crossref","unstructured":"Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M (2018) A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp 6450\u20136459","DOI":"10.1109\/CVPR.2018.00675"},{"issue":"1","key":"3649_CR41","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TMM.2019.2922129","volume":"22","author":"G Tu","year":"2019","unstructured":"Tu G, Fu Y, Li B, Gao J, Jiang YG, Xue X (2019) A multi-task neural approach for emotion attribution, classification, and summarization. IEEE Transactions on Multimedia 22(1):148\u2013159","journal-title":"IEEE Transactions on Multimedia"},{"key":"3649_CR42","doi-asserted-by":"crossref","unstructured":"Uppal H, Sepas-Moghaddam A, Greenspan M, Etemad A (2021) Teacher-student adversarial depth hallucination to improve face recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 3671\u20133680","DOI":"10.1109\/ICCV48922.2021.00365"},{"key":"3649_CR43","doi-asserted-by":"crossref","unstructured":"Wang C, Wang S, Liang G (2019) Identity-and pose-robust facial expression recognition through adversarial feature learning. In: Proceedings of the 27th ACM international conference on multimedia, pp 238\u2013246","DOI":"10.1145\/3343031.3350872"},{"key":"3649_CR44","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1016\/j.neucom.2015.10.112","volume":"175","author":"H Wang","year":"2016","unstructured":"Wang H, Chai X, Chen X (2016) Sparse observation (so) alignment for sign language recognition. Neurocomputing 175:674\u2013685","journal-title":"Neurocomputing"},{"key":"3649_CR45","unstructured":"Wang H, Chai X, Zhou Y, Chen X (2015) Fast sign language recognition benefited from low rank approximation. In: 2015 11Th IEEE international conference and workshops on automatic face and gesture recognition (FG), vol 1, IEEE, pp 1\u20136"},{"key":"3649_CR46","doi-asserted-by":"crossref","unstructured":"Wang H, Gong D, Li Z, Liu W (2019) Decorrelated adversarial learning for age-invariant face recognition","DOI":"10.1109\/CVPR.2019.00364"},{"key":"3649_CR47","doi-asserted-by":"crossref","unstructured":"Wang H, Klaser A, Schmid C, Cheng-Lin L (2011) Action recognition by dense trajectories. In: 2011 IEEE Conference on computer vision and pattern recognition (CVPR), pp 3169\u2013 3176","DOI":"10.1109\/CVPR.2011.5995407"},{"key":"3649_CR48","doi-asserted-by":"crossref","unstructured":"Wang L, Xiong Y, Wang Z, Qiao Y, Lin D, Tang X, Van Gool L (2016) Temporal segment networks: Towards good practices for deep action recognition. In: European conference on computer vision, Springer, pp 20\u201336","DOI":"10.1007\/978-3-319-46484-8_2"},{"issue":"2","key":"3649_CR49","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1515\/cogl.2004.005","volume":"15","author":"S Wilcox","year":"2004","unstructured":"Wilcox S (2004) Cognitive iconicity: Conceptual spaces, meaning, and gesture in signed language. Cognitive Linguistics 15(2):119\u2013147","journal-title":"Cognitive Linguistics"},{"key":"3649_CR50","doi-asserted-by":"crossref","unstructured":"Wu D, Chen J, Sharma N, Pan S, Long G, Blumenstein M (2019) Adversarial action data augmentation for similar gesture action recognition. In: 2019 International joint conference on neural networks (IJCNN), IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2019.8851993"},{"key":"3649_CR51","doi-asserted-by":"crossref","unstructured":"Wu D, Pigou L, Kindermans PJ, Le NDH, Shao L, Dambre J, Odobez JM (2016) Deep dynamic neural networks for multimodal gesture segmentation and recognition. IEEE Trans Pattern Anal Mach Intell 38(8):1583\u20131597","DOI":"10.1109\/TPAMI.2016.2537340"},{"issue":"3","key":"3649_CR52","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TMM.2019.2933330","volume":"22","author":"X Xia","year":"2019","unstructured":"Xia X, Togneri R, Sohel F, Zhao Y, Huang D (2019) Multi-task learning for acoustic event detection using event and frame position information. IEEE Trans Multimed 22(3):569\u2013578","journal-title":"IEEE Trans Multimed"},{"issue":"6","key":"3649_CR53","doi-asserted-by":"publisher","first-page":"3506","DOI":"10.1007\/s10489-020-02042-2","volume":"51","author":"W Xu","year":"2021","unstructured":"Xu W, Li S, Lu Y (2021) Usr-mtl: an unsupervised sentence representation learning framework with multi-task learning. Appl Intell 51(6):3506\u20133521","journal-title":"Appl Intell"},{"key":"3649_CR54","doi-asserted-by":"crossref","unstructured":"Yang C, Xu Y, Shi J, Dai B, Zhou B (2020) Temporal pyramid network for action recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 591\u2013600","DOI":"10.1109\/CVPR42600.2020.00067"},{"key":"3649_CR55","unstructured":"Yang Y, Hospedales TM (2016) Trace norm regularised deep multi-task learning. arXiv:1606.04038"},{"issue":"107","key":"3649_CR56","first-page":"659","volume":"111","author":"H Zhang","year":"2021","unstructured":"Zhang H, Hu Z, Qin W, Xu M, Wang M (2021) Adversarial co-distillation learning for image recognition. Pattern Recogn 111(107):659","journal-title":"Pattern Recogn"},{"key":"3649_CR57","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhou W, Xie C, Pu J, Li H (2016) Chinese sign language recognition with adaptive hmm. In: 2016 IEEE International conference on multimedia and expo (ICME), IEEE, pp 1\u20136","DOI":"10.1109\/ICME.2016.7552950"},{"key":"3649_CR58","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang Y, Zhou W, Wang H, Ouyang X (2021) Multi-city traffic flow forecasting via multi-task learning. Appl Intell, pp 1\u201319","DOI":"10.1007\/s10489-020-02074-8"},{"key":"3649_CR59","doi-asserted-by":"crossref","unstructured":"Zhang Z, Luo P, Loy CC, Tang X (2014) Facial landmark detection by deep multi-task learning European conference on computer vision, Springer, pp 94\u2013108","DOI":"10.1007\/978-3-319-10599-4_7"},{"issue":"5","key":"3649_CR60","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1109\/TPAMI.2015.2469286","volume":"38","author":"Z Zhang","year":"2015","unstructured":"Zhang Z, Luo P, Loy CC, Tang X (2015) Learning deep representation for face alignment with auxiliary attributes. IEEE Trans Pattern Anal Mach Intell 38(5):918\u2013930","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3649_CR61","doi-asserted-by":"crossref","unstructured":"Zhou H, Zhou W, Zhou Y, Li H (2020) Spatial-temporal multi-cue network for continuous sign language recognition. arXiv:2002.03187","DOI":"10.1109\/ICME.2019.00223"},{"key":"3649_CR62","doi-asserted-by":"publisher","first-page":"3367","DOI":"10.1007\/s10489-020-01760-x","volume":"50","author":"J Zhou","year":"2020","unstructured":"Zhou J, Huang JX, Hu QV, He L (2020) Is position important? deep multi-task learning for aspect-based sentiment analysis. Appl Intell 50:3367\u20133378","journal-title":"Appl Intell"},{"key":"3649_CR63","doi-asserted-by":"crossref","unstructured":"Zhu X, Xu C, Hui L, Lu C, Tao D (2019) Approximated bilinear modules for temporal modeling. In: Proceedings of the IEEE international conference on computer vision, pp 3494\u20133503","DOI":"10.1109\/ICCV.2019.00359"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03649-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03649-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03649-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T06:59:06Z","timestamp":1675234746000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03649-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,8]]},"references-count":63,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["3649"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03649-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,8]]},"assertion":[{"value":"16 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2022","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}