{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T06:32:15Z","timestamp":1756189935090,"version":"3.41.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09669-0","type":"journal-article","created":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T22:03:38Z","timestamp":1751839418000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cross-attention multi branch for Vietnamese sign language recognition: CrossViViT"],"prefix":"10.1007","volume":"28","author":[{"given":"Minh Hoang","family":"Chu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoang Diep","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thi Ngoc Anh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoai Nam","family":"Vu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"issue":"13","key":"9669_CR1","doi-asserted-by":"publisher","first-page":"15065","DOI":"10.1109\/JSEN.2021.3074642","volume":"21","author":"K Nguyen-Trong","year":"2021","unstructured":"Nguyen-Trong K, Vu HN, Trung NN, Pham C. Gesture recognition using wearable sensors with bi-long short-term memory convolutional neural networks. IEEE Sens J. 2021;21(13):15065\u201379.","journal-title":"IEEE Sens J"},{"key":"9669_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107561","volume":"108","author":"LM Dang","year":"2020","unstructured":"Dang LM, Min K, Wang H, Piran MJ, Lee CH, Moon H. Sensor-based and vision-based human activity recognition: a comprehensive survey. Pattern Recogn. 2020;108: 107561.","journal-title":"Pattern Recogn"},{"issue":"5","key":"9669_CR3","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.3390\/s19051005","volume":"19","author":"H-B Zhang","year":"2019","unstructured":"Zhang H-B, Zhang Y-X, Zhong B, Lei Q, Yang L, Du J-X, Chen D-S. A comprehensive survey of vision-based human action recognition methods. Sensors. 2019;19(5):1005.","journal-title":"Sensors"},{"issue":"12","key":"9669_CR4","doi-asserted-by":"publisher","first-page":"13029","DOI":"10.1109\/JSEN.2021.3069927","volume":"21","author":"E Ramanujam","year":"2021","unstructured":"Ramanujam E, Perumal T, Padmavathi S. Human activity recognition with smartphone and wearable sensors using deep learning techniques: a review. IEEE Sens J. 2021;21(12):13029\u201340.","journal-title":"IEEE Sens J"},{"key":"9669_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/4770143","volume":"2021","author":"W Tong","year":"2021","unstructured":"Tong W, Li R, Gong X, Zhai S, Zheng X, Ye G. Exploiting serialized fine-grained action recognition using WiFi sensing. Mobile Inform Syst. 2021;2021:1.","journal-title":"Mobile Inform Syst"},{"key":"9669_CR6","doi-asserted-by":"crossref","unstructured":"Khaertdinov B, Ghaleb E, Asteriadis S. Contrastive self-supervised learning for sensor-based human activity recognition. In: 2021 IEEE international joint conference on biometrics (IJCB). IEEE; 2021. p. 1\u20138.","DOI":"10.1109\/IJCB52358.2021.9484410"},{"key":"9669_CR7","first-page":"1","volume":"2023","author":"HN Vu","year":"2023","unstructured":"Vu HN, Hoang T, Tran C, Pham C. Sign language recognition with self-learning fusion model. IEEE Sens J. 2023;2023:1.","journal-title":"IEEE Sens J"},{"key":"9669_CR8","doi-asserted-by":"crossref","unstructured":"Koller O, Zargaran S, Ney H, Bowden R. Deep sign: hybrid CNN-HMM for continuous sign language recognition. In: BMVC; 2016. p. 136\u20131.","DOI":"10.5244\/C.30.136"},{"issue":"16","key":"9669_CR9","doi-asserted-by":"publisher","first-page":"5959","DOI":"10.3390\/s22165959","volume":"22","author":"Y Ma","year":"2022","unstructured":"Ma Y, Xu T, Kim K. Two-stream mixed convolutional neural network for American sign language recognition. Sensors. 2022;22(16):5959.","journal-title":"Sensors"},{"key":"9669_CR10","unstructured":"Camgoz NC, Koller O, Hadfield S, Bowden R. Sign language transformers: joint end-to-end sign language recognition and translation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2020. p. 10023\u201333."},{"key":"9669_CR11","doi-asserted-by":"crossref","unstructured":"Zhao W, Hu H, Zhou W, Shi J, Li H. Best: Bert pre-training for sign language recognition with coupling tokenization. In: Proceedings of the AAAI conference on artificial intelligence, vol. 37; 2023. p. 3597\u2013605.","DOI":"10.1609\/aaai.v37i3.25470"},{"key":"9669_CR12","doi-asserted-by":"publisher","DOI":"10.1145\/3530989","author":"E Rajalakshmi","year":"2022","unstructured":"Rajalakshmi E, Elakkiya R, Prikhodko AL, Grif MG, Bakaev MA, Saini JR, Kotecha K, Subramaniyaswamy V. Static and dynamic isolated Indian and Russian sign language recognition with spatial and temporal feature detection using hybrid neural network. ACM Trans Asian Low Resour Lang Inf Process. 2022. https:\/\/doi.org\/10.1145\/3530989.","journal-title":"ACM Trans Asian Low Resour Lang Inf Process"},{"key":"9669_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2022.3210543","volume":"PP","author":"N Balasubramanian","year":"2022","unstructured":"Balasubramanian N, Elangovan RRE, Kotecha K, Abraham A, Subramaniyaswamy V. Development of an end-to-end deep learning framework for sign language recognition, translation, and video generation. IEEE Access. 2022;PP:1. https:\/\/doi.org\/10.1109\/ACCESS.2022.3210543.","journal-title":"IEEE Access"},{"key":"9669_CR14","doi-asserted-by":"crossref","unstructured":"Arnab A, Dehghani M, Heigold G, Sun C, Lu\u010di\u0107 M, Schmid C. Vivit: a video vision transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision; 2021. p. 6836\u201346.","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"9669_CR15","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Fan H, Malik J, He K. Slowfast networks for video recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision; 2019. p. 6202\u201311.","DOI":"10.1109\/ICCV.2019.00630"},{"key":"9669_CR16","doi-asserted-by":"crossref","unstructured":"S\u00fcmb\u00fcl H. A novel mems and flex sensor-based hand gesture recognition and regenerating system using deep learning model. IEEE Access; 2024. Publisher: IEEE. Accessed 2024-11-04.","DOI":"10.1109\/ACCESS.2024.3448232"},{"issue":"2","key":"9669_CR17","doi-asserted-by":"publisher","first-page":"453","DOI":"10.3390\/s24020453","volume":"24","author":"Y Gu","year":"2024","unstructured":"Gu Y, Oku H, Todoh M. American sign language recognition and translation using perception neuron wearable inertial motion capture system. Sensors. 2024;24(2):453 (Publisher: MDPI. Accessed 2024-11-04).","journal-title":"Sensors"},{"key":"9669_CR18","doi-asserted-by":"crossref","unstructured":"Begum H, Chowdhury O, Hridoy MSR, Islam MM. AI-based sensory glove system to recognize Bengali sign language (BaSL). IEEE Access; 2024. Publisher: IEEE. Accessed 2024-11-04.","DOI":"10.1109\/ACCESS.2024.3472469"},{"issue":"1","key":"9669_CR19","doi-asserted-by":"publisher","first-page":"5378","DOI":"10.1038\/s41467-021-25637-w","volume":"12","author":"F Wen","year":"2021","unstructured":"Wen F, Zhang Z, He T, Lee C. AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove. Nat Commun. 2021;12(1):5378 (Publisher: Nature Publishing Group UK London. Accessed 2024-11-04).","journal-title":"Nat Commun"},{"key":"9669_CR20","unstructured":"Feng D, Zhou C, Huang J, Luo G, Wu X. Design and implementation of gesture recognition system based on flex sensors. IEEE Sens J. 2023. Publisher: IEEE. Accessed 2024-11-04."},{"issue":"36","key":"9669_CR21","doi-asserted-by":"publisher","first-page":"2303504","DOI":"10.1002\/adfm.202303504.","volume":"33","author":"X Wu","year":"2023","unstructured":"Wu X, Luo X, Song Z, Bai Y, Zhang B, Zhang G. Ultra-robust and sensitive flexible strain sensor for real-time and wearable sign language translation. Adv Func Mater. 2023;33(36):2303504. https:\/\/doi.org\/10.1002\/adfm.202303504. (Accessed 2024-11-04).","journal-title":"Adv Func Mater"},{"issue":"1","key":"9669_CR22","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s42979-020-00396-5.","volume":"2","author":"F Pezzuoli","year":"2021","unstructured":"Pezzuoli F, Corona D, Corradini ML. Recognition and classification of dynamic hand gestures by a wearable data-glove. SN Comput Sci. 2021;2(1):5. https:\/\/doi.org\/10.1007\/s42979-020-00396-5. (Accessed 2024-11-04).","journal-title":"SN Comput Sci"},{"issue":"9","key":"9669_CR23","doi-asserted-by":"publisher","first-page":"9210","DOI":"10.1109\/JSEN.2023.3262359","volume":"23","author":"A Byberi","year":"2023","unstructured":"Byberi A, Ravan M, Amineh RK. GloveSense: a hand gesture recognition system based on inductive sensing. IEEE Sens J. 2023;23(9):9210\u20139 (Publisher: IEEE. Accessed 2024-11-04).","journal-title":"IEEE Sens J"},{"issue":"3","key":"9669_CR24","doi-asserted-by":"publisher","first-page":"613","DOI":"10.3390\/electronics12030613","volume":"12","author":"C Lu","year":"2023","unstructured":"Lu C, Amino S, Jing L. Data glove with bending sensor and inertial sensor based on weighted DTW fusion for sign language recognition. Electronics. 2023;12(3):613 (Publisher: MDPI. Accessed 2024-11-04).","journal-title":"Electronics"},{"issue":"8","key":"9669_CR25","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.3390\/electronics12081904","volume":"12","author":"MS Amin","year":"2023","unstructured":"Amin MS, Rizvi STH, Mazzei A, Anselma L. Assistive data glove for isolated static postures recognition in American sign language using neural network. Electronics. 2023;12(8):1904 (Publisher: MDPI. Accessed 2024-11-04).","journal-title":"Electronics"},{"issue":"9","key":"9669_CR26","doi-asserted-by":"publisher","first-page":"5544","DOI":"10.3390\/app13095544","volume":"13","author":"B Shi","year":"2023","unstructured":"Shi B, Chen X, He Z, Sun H, Han R. Research on gesture recognition system using multiple sensors based on earth\u2019s magnetic field and 1D convolution neural network. Appl Sci. 2023;13(9):5544 (Publisher: MDPI. Accessed 2024-11-04).","journal-title":"Appl Sci"},{"issue":"4","key":"9669_CR27","doi-asserted-by":"publisher","first-page":"10589","DOI":"10.1109\/LRA.2022.3191232","volume":"7","author":"J DelPreto","year":"2022","unstructured":"DelPreto J, Hughes J, D\u2019Aria M, De Fazio M, Rus D. A wearable smart glove and its application of pose and gesture detection to sign language classification. IEEE Robot Autom Lett. 2022;7(4):10589\u201396 (Publisher: IEEE. Accessed 2024-11-04).","journal-title":"IEEE Robot Autom Lett"},{"key":"9669_CR28","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.962141","volume":"16","author":"Y Gu","year":"2022","unstructured":"Gu Y, Zheng C, Todoh M, Zha F. American sign language translation using wearable inertial and electromyography sensors for tracking hand movements and facial expressions. Front Neurosci. 2022;16: 962141 (Publisher: Frontiers Media SA. Accessed 2024-11-04).","journal-title":"Front Neurosci"},{"issue":"12","key":"9669_CR29","doi-asserted-by":"publisher","first-page":"8818","DOI":"10.1109\/TII.2022.3152214","volume":"18","author":"M Lee","year":"2022","unstructured":"Lee M, Bae J. Real-time gesture recognition in the view of repeating characteristics of sign languages. IEEE Trans Industr Inf. 2022;18(12):8818\u201328 (Publisher: IEEE. Accessed 2024-11-04).","journal-title":"IEEE Trans Industr Inf"},{"issue":"31","key":"9669_CR30","doi-asserted-by":"publisher","first-page":"22177","DOI":"10.1007\/s11042-020-08961-z","volume":"79","author":"N Aloysius","year":"2020","unstructured":"Aloysius N, Geetha M. Understanding vision-based continuous sign language recognition. Multimed Tools Appl. 2020;79(31):22177\u2013209.","journal-title":"Multimed Tools Appl"},{"key":"9669_CR31","doi-asserted-by":"crossref","unstructured":"Zhang LG, Chen Y, Fang G, Chen X, Gao W. A vision-based sign language recognition system using tied-mixture density hmm. In: Proceedings of the 6th international conference on multimodal interfaces; 2004. p. 198\u2013204.","DOI":"10.1145\/1027933.1027967"},{"key":"9669_CR32","doi-asserted-by":"crossref","unstructured":"Pansare JR, Ingle M. Vision-based approach for American sign language recognition using edge orientation histogram. In: 2016 international conference on image, vision and computing (ICIVC). IEEE; 2016. p. 86\u201390.","DOI":"10.1109\/ICIVC.2016.7571278"},{"issue":"1","key":"9669_CR33","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/s12046-019-1250-6.","volume":"45","author":"T Raghuveera","year":"2020","unstructured":"Raghuveera T, Deepthi R, Mangalashri R, Akshaya R. A depth-based Indian sign language recognition using Microsoft Kinect. S\u0101dhan\u0101. 2020;45(1):34. https:\/\/doi.org\/10.1007\/s12046-019-1250-6. (Accessed 2024-11-04).","journal-title":"S\u0101dhan\u0101"},{"issue":"2","key":"9669_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.101934","volume":"36","author":"S Arooj","year":"2024","unstructured":"Arooj S, Altaf S, Ahmad S, Mahmoud H, Mohamed ASN. Enhancing sign language recognition using CNN and SIFT: a case study on Pakistan sign language. J King Saud Univ Comput Inform Sci. 2024;36(2): 101934 (Publisher: Elsevier. Accessed 2024-11-04).","journal-title":"J King Saud Univ Comput Inform Sci"},{"issue":"10","key":"9669_CR35","doi-asserted-by":"publisher","first-page":"700","DOI":"10.4236\/eng.2015.710061","volume":"07","author":"F Sol\u00eds","year":"2015","unstructured":"Sol\u00eds F, Toxqui C, Mart\u00ednez D. Mexican sign language recognition using Jacobi\u2013Fourier moments. Engineering. 2015;07(10):700\u20135. https:\/\/doi.org\/10.4236\/eng.2015.710061. (Accessed 2024-11-04).","journal-title":"Engineering"},{"key":"9669_CR36","doi-asserted-by":"crossref","unstructured":"Shin J, Musa\u00a0Miah AS, Hasan MAM, Hirooka K, Suzuki K, Lee HS, Jang SW. Korean sign language recognition using transformer-based deep neural network. Appl Sci. 2023;13(5), 3029. Publisher: MDPI. Accessed 2024-11-04","DOI":"10.3390\/app13053029"},{"key":"9669_CR37","doi-asserted-by":"crossref","unstructured":"Pu J, Zhou W, Li H. Iterative alignment network for continuous sign language recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2019. p 4165\u201374. http:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Pu_Iterative_Alignment_Network_for_Continuous_Sign_Language_Recognition_CVPR_2019_paper.html. Accessed 2024-11-04.","DOI":"10.1109\/CVPR.2019.00429"},{"issue":"7","key":"9669_CR38","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. A deep neural framework for continuous sign language recognition by iterative training. IEEE Trans Multimed. 2019;21(7):1880\u201391 (Publisher: IEEE. Accessed 2024-11-04).","journal-title":"IEEE Trans Multimed"},{"key":"9669_CR39","doi-asserted-by":"crossref","unstructured":"Liu A, Tan Z, Yu Z, Zhao C, Wan J, Liang Y, Lei Z, Zhang D, Li SZ, Guo G. FM-ViT: flexible modal vision transformers for face anti-spoofing; 2023. arxiv.org\/abs\/2305.03277.","DOI":"10.1109\/TIFS.2023.3296330"},{"key":"9669_CR40","doi-asserted-by":"publisher","DOI":"10.1145\/3640817","author":"A Antil","year":"2024","unstructured":"Antil A, Dhiman C. Mf2shrt: multimodal feature fusion using shared layered transformer for face anti-spoofing. ACM Trans Multimedia Comput Commun Appl. 2024. https:\/\/doi.org\/10.1145\/3640817.","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"key":"9669_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128992","volume":"617","author":"A Antil","year":"2025","unstructured":"Antil A, Dhiman C. Unmasking deception: a comprehensive survey on the evolution of face anti-spoofing methods. Neurocomputing. 2025;617: 128992. https:\/\/doi.org\/10.1016\/j.neucom.2024.128992.","journal-title":"Neurocomputing"},{"key":"9669_CR42","unstructured":"De\u00a0Coster M, Van\u00a0Herreweghe M, Dambre J. Sign language recognition with transformer networks. In: Proceedings of the twelfth language resources and evaluation conference; 2020, p. 6018\u201324. https:\/\/aclanthology.org\/2020.lrec-1.737\/. Accessed 2024-11-04."},{"key":"9669_CR43","doi-asserted-by":"publisher","first-page":"4730","DOI":"10.1109\/ACCESS.2022.3231130","volume":"11","author":"DR Kothadiya","year":"2023","unstructured":"Kothadiya DR, Bhatt CM, Saba T, Rehman A, Bahaj SA. SIGNFORMER: deepvision transformer for sign language recognition. IEEE Access. 2023;11:4730\u20139 (Publisher: IEEE. Accessed 2024-11-04).","journal-title":"IEEE Access"},{"key":"9669_CR44","unstructured":"Pu DM, Lim MK, Chong CY. Siformer: feature-isolated transformer for efficient skeleton-based sign language recognition. In: ACM multimedia 2024; 2024. https:\/\/openreview.net\/forum?id=12hscviLrW. Accessed 2024-11-05."},{"key":"9669_CR45","doi-asserted-by":"publisher","unstructured":"Rastgoo R, Kiani K, Escalera S. A transformer model for boundary detection in continuous sign language. Multimed Tools Appl. 2024. https:\/\/doi.org\/10.1007\/s11042-024-19079-x. Accessed 2024-11-05.","DOI":"10.1007\/s11042-024-19079-x"},{"key":"9669_CR46","unstructured":"Ghadami A, Taheri A, Meghdari A. A transformer-based multi-stream approach for isolated Iranian sign language recognition; 2024. arXiv:2407.09544. Accessed 2024-11-05."},{"key":"9669_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107268","volume":"102","author":"E Rajasekar","year":"2025","unstructured":"Rajasekar E, Chandra H, Pears N, Vairavasundaram S, Kotecha K. Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning. Biomed Signal Process Control. 2025;102: 107268. https:\/\/doi.org\/10.1016\/j.bspc.2024.107268.","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"9669_CR48","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1186\/S40537-024-01016-2","volume":"11","author":"J Kumarappan","year":"2024","unstructured":"Kumarappan J, Rajasekar E, Vairavasundaram S, Kotecha K, Kulkarni A. Siamese graph convolutional split-attention network with NLP based social sentimental data for enhanced stock price predictions. J Big Data. 2024;11(1):154. https:\/\/doi.org\/10.1186\/S40537-024-01016-2.","journal-title":"J Big Data"},{"key":"9669_CR49","doi-asserted-by":"publisher","DOI":"10.1007\/s44196-024-00680-9","author":"J Kumarappan","year":"2024","unstructured":"Kumarappan J, Vairavasundaram S, Kotecha K, Kulkarni A. Federated learning enhanced MLP-LSTM modeling in an integrated deep learning pipeline for stock market prediction. Int J Comput Intell Syst. 2024. https:\/\/doi.org\/10.1007\/s44196-024-00680-9.","journal-title":"Int J Comput Intell Syst"},{"key":"9669_CR50","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N. An image is worth [CDATA[16\\times 16]]$$16\\times 16$$ words: transformers for image recognition at scale; 2021. arxiv.org\/abs\/2010.11929."},{"key":"9669_CR51","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding; 2019. arxiv.org\/abs\/1810.04805."},{"key":"9669_CR52","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala SK, Girshic, RB, Farhadi A. You only look once: unified, real-time object detection. CoRR arXiv:abs\/1506.02640; 2015.","DOI":"10.1109\/CVPR.2016.91"},{"key":"9669_CR53","unstructured":"Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Viola F, Green T, Back T, Natsev P, Suleyman M, Zisserman A. The kinetics human action video dataset; 2017. arxiv.org\/abs\/1705.06950."},{"key":"9669_CR54","doi-asserted-by":"crossref","unstructured":"Li D, Opazo CR, Yu X, Li H. Word-level deep sign language recognition from video: a new large-scale dataset and methods comparison; 2020. arxiv.org\/abs\/1910.11006.","DOI":"10.1109\/WACV45572.2020.9093512"},{"key":"9669_CR55","doi-asserted-by":"crossref","unstructured":"Hu H, Zhao W, Zhou W, Wang Y, Li H. SignBERT: pre-training of hand-model-aware representation for sign language recognition; 2021. arxiv.org\/abs\/2110.05382.","DOI":"10.1109\/ICCV48922.2021.01090"},{"key":"9669_CR56","doi-asserted-by":"publisher","unstructured":"Pu M, Lim MK, Chong CY. Siformer: feature-isolated transformer for efficient skeleton-based sign language recognition. In: Proceedings of the 32nd ACM international conference on multimedia. MM\u201924., New York, NY, USA: Association for Computing Machinery; 2024. p. 9387\u201396. https:\/\/doi.org\/10.1145\/3664647.3681578.","DOI":"10.1145\/3664647.3681578"},{"key":"9669_CR57","doi-asserted-by":"publisher","unstructured":"Hosain AA, Selvam\u00a0Santhalingam P, Pathak P, Rangwala H, Kosecka J. Hand pose guided 3D pooling for word-level sign language recognition. In: 2021 IEEE winter conference on applications of computer vision (WACV); 2021. p. 3428\u201338. https:\/\/doi.org\/10.1109\/WACV48630.2021.00347.","DOI":"10.1109\/WACV48630.2021.00347"},{"key":"9669_CR58","doi-asserted-by":"crossref","unstructured":"Tunga A, Nuthalapati SV, Wachs J. Pose-based sign language recognition using GCN and BERT; 2020. arXiv:abs\/2012.00781.","DOI":"10.1109\/WACVW52041.2021.00008"},{"key":"9669_CR59","doi-asserted-by":"publisher","first-page":"12481","DOI":"10.1007\/s00521-023-08380-9","volume":"35","author":"Z Guo","year":"2023","unstructured":"Guo Z, Hou Y, Li W. Sign language recognition via dimensional global-local shift and cross-scale aggregation. Neural Comput Appl. 2023;35:12481\u201393. https:\/\/doi.org\/10.1007\/s00521-023-08380-9.","journal-title":"Neural Comput Appl"},{"key":"9669_CR60","first-page":"1","volume":"2023","author":"L Gabralla","year":"2023","unstructured":"Gabralla L. Multi-semantic discriminative feature learning for sign gesture recognition using hybrid deep neural architecture. IEEE Access. 2023;2023:1.","journal-title":"IEEE Access"},{"key":"9669_CR61","doi-asserted-by":"publisher","DOI":"10.1145\/3656046","author":"X Shen","year":"2024","unstructured":"Shen X, Zheng Z, Yang Y. Stepnet: spatial\u2013temporal part-aware network for isolated sign language recognition. ACM Trans Multimedia Comput Commun Appl. 2024. https:\/\/doi.org\/10.1145\/3656046.","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"key":"9669_CR62","unstructured":"Zhou B, Andonian A, Oliva A, Torralba A. Temporal relational reasoning in videos; 2018. p. 803\u201318. arXiv:abs\/1711.08496."},{"key":"9669_CR63","unstructured":"Bertasius G, Wang H, Torresani L. Is space-time attention all you need for video understanding? 2021 2(3). p. 4. arxiv.org\/abs\/2102.05095."},{"key":"9669_CR64","doi-asserted-by":"crossref","unstructured":"Lin J, Gan C, Han S. TSM: temporal shift module for efficient video understanding; 2019. arxiv.org\/abs\/1811.08383.","DOI":"10.1109\/ICCV.2019.00718"},{"key":"9669_CR65","unstructured":"Li Z, Zhou W, Zhao W, Wu K, Hu H, Li H. Uni-sign: toward unified sign language understanding at scale; 2025. arxiv.org\/abs\/2501.15187."}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09669-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09669-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09669-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T22:03:54Z","timestamp":1751839434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09669-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,7]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9669"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09669-0","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,7]]},"assertion":[{"value":"25 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"141"}}