{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:21:04Z","timestamp":1770830464056,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"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":["Vis Comput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00371-023-03211-3","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T11:02:34Z","timestamp":1703588554000},"page":"7845-7858","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["KSRB-Net: a continuous sign language recognition deep learning strategy based on motion perception mechanism"],"prefix":"10.1007","volume":"40","author":[{"given":"Feng","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunrui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7844-6035","authenticated-orcid":false,"given":"Jianhua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"3211_CR1","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, pp. 4165\u20134174 (2019)","DOI":"10.1109\/CVPR.2019.00429"},{"key":"3211_CR2","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.cviu.2015.09.013","volume":"141","author":"O Koller","year":"2015","unstructured":"Koller, O., Forster, J., Ney, H.: Continuous sign language recognition: towards large vocabulary statistical recognition systems handling multiple signers. Comput. Vis. Image Underst. 141, 108\u2013125 (2015)","journal-title":"Comput. Vis. Image Underst."},{"key":"3211_CR3","unstructured":"Yuan, T.: TJUT Sign Language Recognition and Translation Dataset. (2020). http:\/\/ylr.tjut.edu.cn\/kxyj.htm"},{"issue":"12","key":"3211_CR4","doi-asserted-by":"publisher","first-page":"3278","DOI":"10.1109\/TNNLS.2015.2470175","volume":"26","author":"Q Yang","year":"2015","unstructured":"Yang, Q., Jagannathan, S., Sun, Y.: Robust integral of neural network and error sign control of MIMO nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 26(12), 3278\u20133286 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"3211_CR5","doi-asserted-by":"publisher","first-page":"4800","DOI":"10.1109\/TNNLS.2021.3061115","volume":"33","author":"C Li","year":"2021","unstructured":"Li, C., Xie, C., Zhang, B., Han, J., Zhen, X., Chen, J.: Memory attention networks for skeleton-based action recognition. IEEE Trans. Neural Netw. Learn. Syst. 33(9), 4800\u20134814 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3211_CR6","doi-asserted-by":"crossref","unstructured":"Slimane, F.B., Bouguessa, M.: Context matters: Self-attention for sign language recognition. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 7884\u20137891 (2021). IEEE","DOI":"10.1109\/ICPR48806.2021.9412916"},{"key":"3211_CR7","doi-asserted-by":"crossref","unstructured":"Xiao, F., Liu, R., Yuan, T., Fan, Z., Wang, J., Zhang, J.: Slrformer: Continuous sign language recognition based on vision transformer. In: 2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 1\u20137 (2022). IEEE Computer Society","DOI":"10.1109\/ACIIW57231.2022.10086026"},{"key":"3211_CR8","doi-asserted-by":"crossref","unstructured":"Liu, T., Zhou, W., Li, H.: Sign language recognition with long short-term memory. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2871\u20132875 (2016). IEEE","DOI":"10.1109\/ICIP.2016.7532884"},{"key":"3211_CR9","doi-asserted-by":"crossref","unstructured":"Huang, J., Zhou, W., Zhang, Q., Li, H., Li, W.: Video-based sign language recognition without temporal segmentation. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11903"},{"key":"3211_CR10","doi-asserted-by":"crossref","unstructured":"Guo, D., Zhou, W., Wang, M., Li, H.: Sign language recognition based on adaptive hmms with data augmentation. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2876\u20132880 (2016). IEEE","DOI":"10.1109\/ICIP.2016.7532885"},{"key":"3211_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhou, W., Xie, C., Pu, J., Li, H.: Chinese sign language recognition with adaptive hmm. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2016). IEEE","DOI":"10.1109\/ICME.2016.7552950"},{"key":"3211_CR12","doi-asserted-by":"crossref","unstructured":"Guo, D., Zhou, W., Li, H., Wang, M.: 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 (2017)","DOI":"10.1145\/3152121"},{"key":"3211_CR13","doi-asserted-by":"crossref","unstructured":"Koller, O., Zargaran, S., Ney, H.: Re-sign: Re-aligned end-to-end sequence modelling with deep recurrent cnn-hmms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4297\u20134305 (2017)","DOI":"10.1109\/CVPR.2017.364"},{"key":"3211_CR14","doi-asserted-by":"crossref","unstructured":"Koller, O., Zargaran, O., Ney, H., Bowden, R.: Deep sign: Hybrid cnn-hmm for continuous sign language recognition. In: Proceedings of the British Machine Vision Conference 2016 (2016)","DOI":"10.5244\/C.30.136"},{"key":"3211_CR15","doi-asserted-by":"crossref","unstructured":"Cihan\u00a0Camgoz, N., Hadfield, S., Koller, O., Bowden, R.: Subunets: End-to-end hand shape and continuous sign language recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3056\u20133065 (2017)","DOI":"10.1109\/ICCV.2017.332"},{"key":"3211_CR16","doi-asserted-by":"crossref","unstructured":"Cui, R., Liu, H., Zhang, C.: Recurrent convolutional neural networks for continuous sign language recognition by staged optimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7361\u20137369 (2017)","DOI":"10.1109\/CVPR.2017.175"},{"key":"3211_CR17","doi-asserted-by":"crossref","unstructured":"Pu, J., Zhou, W., Li, H.: Dilated convolutional network with iterative optimization for continuous sign language recognition. In: IJCAI, vol. 3, p. 7 (2018)","DOI":"10.24963\/ijcai.2018\/123"},{"key":"3211_CR18","doi-asserted-by":"crossref","unstructured":"de Amorim, C.C., Mac\u00eado, D., Zanchettin, C.: Spatial-temporal graph convolutional networks for sign language recognition. In: International Conference on Artificial Neural Networks, pp. 646\u2013657 (2019). Springer","DOI":"10.1007\/978-3-030-30493-5_59"},{"key":"3211_CR19","unstructured":"Yin, K.: Sign language translation with transformers. arXiv preprint arXiv:2004.005882 (2020)"},{"key":"3211_CR20","unstructured":"Camgoz, N.C., 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, pp. 10023\u201310033 (2020)"},{"key":"3211_CR21","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1933\u20131941 (2016)","DOI":"10.1109\/CVPR.2016.213"},{"key":"3211_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., Ji, B., Shi, X., Zhang, J., Kang, B., Wang, L.: Tea: Temporal excitation and aggregation for action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 909\u2013918 (2020)","DOI":"10.1109\/CVPR42600.2020.00099"},{"key":"3211_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhu, G., Shen, P., Song, J., Afaq\u00a0Shah, S., Bennamoun, M.: Learning spatiotemporal features using 3dcnn and convolutional lstm for gesture recognition. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 3120\u20133128 (2017)","DOI":"10.1109\/ICCVW.2017.369"},{"issue":"8","key":"3211_CR24","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1109\/TPAMI.2016.2537340","volume":"38","author":"D Wu","year":"2016","unstructured":"Wu, D., Pigou, L., Kindermans, P.-J., Le, N.D.-H., Shao, L., Dambre, J., Odobez, J.-M.: Deep dynamic neural networks for multimodal gesture segmentation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 38(8), 1583\u20131597 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3211_CR25","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., Kautz, J.: Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4207\u20134215 (2016)","DOI":"10.1109\/CVPR.2016.456"},{"key":"3211_CR26","doi-asserted-by":"crossref","unstructured":"Xiao, F., Shen, C., Yuan, T., Chen, S.: CRB-Net: A sign language recognition deep learning strategy based on multi-modal fusion with attention mechanism. In: SMC, p. (2021)","DOI":"10.1109\/SMC52423.2021.9659090"},{"key":"3211_CR27","doi-asserted-by":"crossref","unstructured":"Chiu, C.-C., Sainath, T.N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R.J., Rao, K., Gonina, E., et al: State-of-the-art speech recognition with sequence-to-sequence models. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4774\u20134778 (2018). IEEE","DOI":"10.1109\/ICASSP.2018.8462105"},{"key":"3211_CR28","doi-asserted-by":"crossref","unstructured":"Liao, M., Zhang, J., Wan, Z., Xie, F., Liang, J., Lyu, P., Yao, C., Bai, X.: Scene text recognition from two-dimensional perspective. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8714\u20138721 (2019)","DOI":"10.1609\/aaai.v33i01.33018714"},{"key":"3211_CR29","doi-asserted-by":"crossref","unstructured":"Meng, F., Zhang, J.: Dtmt: A novel deep transition architecture for neural machine translation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 224\u2013231 (2019)","DOI":"10.1609\/aaai.v33i01.3301224"},{"key":"3211_CR30","doi-asserted-by":"crossref","unstructured":"Jiang, B., Wang, M., Gan, W., Wu, W., Yan, J.: Stm: Spatiotemporal and motion encoding for action recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2000\u20132009 (2019)","DOI":"10.1109\/ICCV.2019.00209"},{"key":"3211_CR31","doi-asserted-by":"crossref","unstructured":"Ning, G., Zhang, Z., Huang, C., Ren, X., Wang, H., Cai, C., He, Z.: Spatially supervised recurrent convolutional neural networks for visual object tracking. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1\u20134 (2017). IEEE","DOI":"10.1109\/ISCAS.2017.8050867"},{"key":"3211_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhou, W., Zhou, Y., Li, H.: Spatial-temporal multi-cue network for continuous sign language recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13009\u201313016 (2020)","DOI":"10.1609\/aaai.v34i07.7001"},{"issue":"7","key":"3211_CR33","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. 21(7), 1880\u20131891 (2019)","journal-title":"IEEE Trans. Multimed."},{"key":"3211_CR34","doi-asserted-by":"crossref","unstructured":"Cheng, K.L., Yang, Z., Chen, Q., Tai, Y.-W.: Fully convolutional networks for continuous sign language recognition. In: European Conference on Computer Vision, pp. 697\u2013714 (2020). Springer","DOI":"10.1007\/978-3-030-58586-0_41"},{"key":"3211_CR35","doi-asserted-by":"crossref","unstructured":"Pu, J., Zhou, W., Hu, H., Li, H.: Boosting continuous sign language recognition via cross modality augmentation. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1497\u20131505 (2020)","DOI":"10.1145\/3394171.3413931"},{"issue":"9","key":"3211_CR36","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1109\/TPAMI.2019.2911077","volume":"42","author":"O Koller","year":"2019","unstructured":"Koller, O., Camgoz, N.C., Ney, H., Bowden, R.: Weakly supervised learning with multi-stream CNN-LSTM-HMMS to discover sequential parallelism in sign language videos. IEEE Trans. Pattern Anal. Mach. Intell. 42(9), 2306\u20132320 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3211_CR37","doi-asserted-by":"crossref","unstructured":"Guo, D., Zhou, W., Li, H., Wang, M.: Hierarchical LSTM for sign language translation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.12235"},{"key":"3211_CR38","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 369\u2013376 (2006)","DOI":"10.1145\/1143844.1143891"},{"key":"3211_CR39","doi-asserted-by":"crossref","unstructured":"Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K.: Sequence to sequence-video to text. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4534\u20134542 (2015)","DOI":"10.1109\/ICCV.2015.515"},{"key":"3211_CR40","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456 (2015). PMLR"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03211-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-03211-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03211-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T16:11:38Z","timestamp":1730909498000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-03211-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"references-count":40,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["3211"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-03211-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"23 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2023","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 declared that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}