{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T04:01:38Z","timestamp":1741838498873,"version":"3.38.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T00:00:00Z","timestamp":1741737600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T00:00:00Z","timestamp":1741737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62076211","62076211"],"award-info":[{"award-number":["62076211","62076211"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the University-Industry Cooperation Programs of Fujian Province of China","award":["2023H6001","2023H6001"],"award-info":[{"award-number":["2023H6001","2023H6001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07119-8","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T03:13:48Z","timestamp":1741749228000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data augmentation and debiasing for signers in signer-independent sign language translation"],"prefix":"10.1007","volume":"81","author":[{"given":"Honghao","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yidong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,12]]},"reference":[{"key":"7119_CR1","doi-asserted-by":"publisher","unstructured":"Sutton-Spence R, Woll B (1999) The linguistics of British sign language: an introduction. Cambridge University Press, 110 Midland Avenue, Port Chester, NY, p 10573 https:\/\/doi.org\/10.1017\/CBO9781139167048","DOI":"10.1017\/CBO9781139167048"},{"key":"7119_CR2","doi-asserted-by":"publisher","unstructured":"Camgoz NC, Hadfield S, Koller O, Ney H, Bowden R (2018) Neural sign language translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7784\u20137793 https:\/\/doi.org\/10.1109\/CVPR.2018.00812","DOI":"10.1109\/CVPR.2018.00812"},{"key":"7119_CR3","doi-asserted-by":"publisher","unstructured":"Camgoz NC, Koller O, Hadfield S, Bowden R (2020) Multi-channel transformers for multi-articulatory sign language translation. In: Computer Vision\u2013ECCV 2020 Workshops: Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IV 16, Springer, pp 301\u2013319 https:\/\/doi.org\/10.1007\/978-3-030-66823-5_18","DOI":"10.1007\/978-3-030-66823-5_18"},{"key":"7119_CR4","doi-asserted-by":"publisher","unstructured":"Camgoz NC, Koller O, Hadfield S, Bowden R (2020) 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\u201310033https:\/\/doi.org\/10.1109\/CVPR42600.2020.01004","DOI":"10.1109\/CVPR42600.2020.01004"},{"key":"7119_CR5","doi-asserted-by":"publisher","unstructured":"Zhou H, Zhou W, Qi W, Pu J, Li H (2021) Improving sign language translation with monolingual data by sign back-translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1316\u20131325 https:\/\/doi.org\/10.1109\/CVPR46437.2021.00137","DOI":"10.1109\/CVPR46437.2021.00137"},{"key":"7119_CR6","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1109\/TMM.2021.3059098","volume":"24","author":"H Zhou","year":"2021","unstructured":"Zhou H, Zhou W, Zhou Y, Li H (2021) Spatial-temporal multi-cue network for sign language recognition and translation. IEEE Trans Multimed 24:768\u2013779. https:\/\/doi.org\/10.1109\/TMM.2021.3059098","journal-title":"IEEE Trans Multimed"},{"key":"7119_CR7","doi-asserted-by":"publisher","unstructured":"Chen Y, Wei F, Sun X, Wu Z, Lin S (2022) A simple multi-modality transfer learning baseline for sign language translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5120\u20135130 https:\/\/doi.org\/10.1109\/CVPR52688.2022.00506","DOI":"10.1109\/CVPR52688.2022.00506"},{"key":"7119_CR8","doi-asserted-by":"publisher","unstructured":"Chen Y, Zuo R, Wei F, Wu Y, LIU S, Mak B (2022) Two-stream network for sign language recognition and translation. In: Advances in neural information processing systems, pp 17043\u201317056 https:\/\/doi.org\/10.5555\/3600270.3601510","DOI":"10.5555\/3600270.3601510"},{"key":"7119_CR9","doi-asserted-by":"publisher","unstructured":"Zhang B, M\u00fcller M, Sennrich R (2023) SLTUNET: a simple unified model for sign language translation. In: The Eleventh International Conference on Learning Representations https:\/\/doi.org\/10.48550\/arXiv.2305.01778","DOI":"10.48550\/arXiv.2305.01778"},{"key":"7119_CR10","doi-asserted-by":"publisher","unstructured":"Fu B, Ye P, Zhang L, Yu P, Hu C, Shi X, Chen Y (2023) A token-level contrastive framework for sign language translation. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1\u20135 . https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10095466","DOI":"10.1109\/ICASSP49357.2023.10095466"},{"key":"7119_CR11","doi-asserted-by":"publisher","unstructured":"Yu P, Zhang L, Fu B, Chen Y (2023) Efficient sign language translation with a curriculum-based non-autoregressive decoder. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pp 5260\u20135268. Main Track https:\/\/doi.org\/10.24963\/ijcai.2023\/584","DOI":"10.24963\/ijcai.2023\/584"},{"key":"7119_CR12","doi-asserted-by":"publisher","unstructured":"Jin T, Zhao Z (2021) Contrastive disentangled meta-learning for signer-independent sign language translation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp 5065\u20135073 https:\/\/doi.org\/10.1145\/3474085.3475456","DOI":"10.1145\/3474085.3475456"},{"key":"7119_CR13","doi-asserted-by":"publisher","unstructured":"Hendrycks D, Dietterich T (2019) Benchmarking neural network robustness to common corruptions and perturbations. arXiv:1903.12261https:\/\/doi.org\/10.48550\/arXiv.1903.12261","DOI":"10.48550\/arXiv.1903.12261"},{"key":"7119_CR14","doi-asserted-by":"publisher","unstructured":"Fu H, Zhang L, Fu B, Zhao R, Su J, Shi X, Chen Y (2024) Signer diversity-driven data augmentation for signer-independent sign language translation. In: Duh K, Gomez H, Bethard S (eds) Findings of the Association for Computational Linguistics: NAACL 2024, pp 2182\u20132193. Association for Computational Linguistics, Mexico City, Mexico . https:\/\/doi.org\/10.18653\/v1\/2024.findings-naacl.140. https:\/\/aclanthology.org\/2024.findings-naacl.140","DOI":"10.18653\/v1\/2024.findings-naacl.140"},{"key":"7119_CR15","doi-asserted-by":"publisher","first-page":"13009","DOI":"10.1109\/TMM.2021.3059098","volume":"34","author":"H Zhou","year":"2020","unstructured":"Zhou H, Zhou W, Zhou Y, Li H (2020) Spatial-temporal multi-cue network for continuous sign language recognition. Proceedings of the AAAI Conference on Artificial Intelligence 34:13009\u201313016. https:\/\/doi.org\/10.1109\/TMM.2021.3059098","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"7119_CR16","doi-asserted-by":"publisher","first-page":"12034","DOI":"10.5555\/3495724.3496733","volume":"33","author":"D Li","year":"2020","unstructured":"Li D, Xu C, Yu X, Zhang K, Swift B, Suominen H, Li H (2020) Tspnet: hierarchical feature learning via temporal semantic pyramid for sign language translation. Adv Neural Inf Process Syst 33:12034\u201312045. https:\/\/doi.org\/10.5555\/3495724.3496733","journal-title":"Adv Neural Inf Process Syst"},{"key":"7119_CR17","doi-asserted-by":"publisher","unstructured":"Ye J, Jiao W, Wang X, Tu Z, Xiong H (2023) Cross-modality data augmentation for end-to-end sign language translation, pp 13558\u201313571 https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.904","DOI":"10.18653\/v1\/2023.findings-emnlp.904"},{"key":"7119_CR18","doi-asserted-by":"publisher","unstructured":"Akuzawa K, Iwasawa Y, Matsuo Y (2020) Adversarial invariant feature learning with accuracy constraint for domain generalization. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, W\u00fcrzburg, Germany, September 16\u201320, 2019, Proceedings, Part II, pp 315\u2013331. https:\/\/doi.org\/10.1007\/978-3-030-46147-8_19 . Springer","DOI":"10.1007\/978-3-030-46147-8_19"},{"key":"7119_CR19","doi-asserted-by":"publisher","unstructured":"Lv F, Liang J, Li S, Zang B, Liu CH, Wang Z, Liu D (2022) Causality inspired representation learning for domain generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 8046\u20138056.https:\/\/doi.org\/10.1109\/CVPR52688.2022.00788","DOI":"10.1109\/CVPR52688.2022.00788"},{"key":"7119_CR20","doi-asserted-by":"publisher","first-page":"16096","DOI":"10.5555\/3495724.3497074","volume":"33","author":"S Zhao","year":"2020","unstructured":"Zhao S, Gong M, Liu T, Fu H, Tao D (2020) Domain generalization via entropy regularization. Adv Neural Inf Process Syst 33:16096\u201316107. https:\/\/doi.org\/10.5555\/3495724.3497074","journal-title":"Adv Neural Inf Process Syst"},{"key":"7119_CR21","doi-asserted-by":"publisher","unstructured":"Matsuura T, Harada T (2020) Domain generalization using a mixture of multiple latent domains. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 11749\u201311756. https:\/\/doi.org\/10.48550\/arXiv.1911.07661","DOI":"10.48550\/arXiv.1911.07661"},{"key":"7119_CR22","doi-asserted-by":"publisher","unstructured":"Li P, Li D, Li W, Gong S, Fu Y, Hospedales TM (2021) A simple feature augmentation for domain generalization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 8886\u20138895 https:\/\/doi.org\/10.1109\/ICCV48922.2021.00876","DOI":"10.1109\/ICCV48922.2021.00876"},{"key":"7119_CR23","doi-asserted-by":"publisher","unstructured":"Wan C, Shen X, Zhang Y, Yin Z, Tian X, Gao F, Huang J, Hua X-S (2022) Meta convolutional neural networks for single domain generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4682\u20134691 https:\/\/doi.org\/10.1109\/CVPR52688.2022.00464","DOI":"10.1109\/CVPR52688.2022.00464"},{"key":"7119_CR24","doi-asserted-by":"publisher","unstructured":"Qu J, Faney T, Wang Z, Gallinari P, Yousef S, Hemptinne J-C (2022) Hmoe: hypernetwork-based mixture of experts for domain generalization. arXiv preprint arXiv:2211.08253https:\/\/doi.org\/10.48550\/arXiv.2211.08253","DOI":"10.48550\/arXiv.2211.08253"},{"key":"7119_CR25","doi-asserted-by":"publisher","unstructured":"Bahng H, Chun S, Yun S, Choo J, Oh SJ (2020) Learning de-biased representations with biased representations. In: International Conference on Machine Learning, pp 528\u2013539, PMLR https:\/\/doi.org\/10.1109\/CVPR52729.2023.00734","DOI":"10.1109\/CVPR52729.2023.00734"},{"key":"7119_CR26","doi-asserted-by":"publisher","unstructured":"Lim J, Kim Y, Kim B, Ahn C, Shin J, Yang E, Han S (2023) Biasadv: bias-adversarial augmentation for model debiasing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3832\u20133841 https:\/\/doi.org\/10.1109\/CVPR52729.2023.00373","DOI":"10.1109\/CVPR52729.2023.00373"},{"key":"7119_CR27","doi-asserted-by":"publisher","unstructured":"Byrd J, Lipton Z (2019) What is the effect of importance weighting in deep learning? In: International Conference on Machine Learning, pp 872\u2013881 PMLR. https:\/\/doi.org\/10.48550\/arXiv.1812.03372","DOI":"10.48550\/arXiv.1812.03372"},{"key":"7119_CR28","doi-asserted-by":"publisher","unstructured":"Jung S, Chun S, Moon T (2022) Learning fair classifiers with partially annotated group labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10348\u201310357 https:\/\/doi.org\/10.1109\/CVPR52688.2022.01010","DOI":"10.1109\/CVPR52688.2022.01010"},{"issue":"5555\/3454287","key":"7119_CR29","first-page":"3454363","volume":"10","author":"R Cadene","year":"2019","unstructured":"Cadene R, Dancette C, Cord M, Parikh D et al (2019) Rubi: reducing unimodal biases for visual question answering. Adv Neural Inf Process Syst doi 10(5555\/3454287):3454363","journal-title":"Adv Neural Inf Process Syst doi"},{"key":"7119_CR30","unstructured":"Creager E, Jacobsen J-H, Zemel R (2021) Environment inference for invariant learning. In: International Conference on Machine Learning, pp 2189\u20132200 PMLR. https:\/\/proceedings.mlr.press\/v139\/creager21a.html"},{"key":"7119_CR31","unstructured":"Liu EZ, Haghgoo B, Chen AS, Raghunathan A, Koh PW, Sagawa S, Liang P, Finn C (2021) Just train twice: improving group robustness without training group information. In: International Conference on Machine Learning, pp 6781\u20136792, PMLR. https:\/\/proceedings.mlr.press\/v139\/liu21f.html"},{"key":"7119_CR32","doi-asserted-by":"publisher","unstructured":"Seo S, Lee J-Y, Han B (2022) Unsupervised learning of debiased representations with pseudo-attributes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 16742\u201316751 https:\/\/doi.org\/10.1109\/CVPR52688.2022.01624","DOI":"10.1109\/CVPR52688.2022.01624"},{"key":"7119_CR33","doi-asserted-by":"publisher","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572https:\/\/doi.org\/10.48550\/arXiv.1412.6572","DOI":"10.48550\/arXiv.1412.6572"},{"issue":"10","key":"7119_CR34","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Mu Y, Tan M, Wang X et al (2020) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43(10):3349\u20133364. https:\/\/doi.org\/10.1109\/TPAMI.2020.2983686","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"7119_CR35","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos R, Jacobsen J-H, Michaelis C, Zemel R, Brendel W, Bethge M, Wichmann FA (2020) Shortcut learning in deep neural networks. Nature Mach Intell 2(11):665\u2013673. https:\/\/doi.org\/10.1038\/s42256-020-00257-z","journal-title":"Nature Mach Intell"},{"key":"7119_CR36","doi-asserted-by":"publisher","first-page":"34405","DOI":"10.5555\/3600270.3602763","volume":"35","author":"M Schiappa","year":"2022","unstructured":"Schiappa M, Vyas S, Palangi H, Rawat Y, Vineet V (2022) Robustness analysis of video-language models against visual and language perturbations. Adv Neural Inf Process Syst 35:34405\u201334420. https:\/\/doi.org\/10.5555\/3600270.3602763","journal-title":"Adv Neural Inf Process Syst"},{"key":"7119_CR37","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1026\u20131034 . https:\/\/doi.org\/10.1109\/ICCV.2015.123","DOI":"10.1109\/ICCV.2015.123"},{"key":"7119_CR38","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"7119_CR39","doi-asserted-by":"publisher","unstructured":"Papineni K, Roukos S, Ward T, Zhu W-J (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp 311\u2013318 https:\/\/doi.org\/10.3115\/1073083.1073135","DOI":"10.3115\/1073083.1073135"},{"key":"7119_CR40","doi-asserted-by":"publisher","unstructured":"Lin C-Y, Och FJ (2004) Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), pp 605\u2013612 https:\/\/doi.org\/10.3115\/1218955.1219032","DOI":"10.3115\/1218955.1219032"},{"key":"7119_CR41","doi-asserted-by":"publisher","unstructured":"Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2019) Autoaugment: Learning augmentation strategies from data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 113\u2013123 https:\/\/doi.org\/10.1109\/CVPR.2019.00020","DOI":"10.1109\/CVPR.2019.00020"},{"key":"7119_CR42","doi-asserted-by":"publisher","unstructured":"Cubuk ED, Zoph B, Shlens J, Le QV (2020) Randaugment: Practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp 702\u2013703 https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00359","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"7119_CR43","doi-asserted-by":"publisher","unstructured":"M\u00fcller SG, Hutter F (2021) Trivialaugment: tuning-free yet state-of-the-art data augmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 774\u2013782 https:\/\/doi.org\/10.1109\/ICCV48922.2021.00081","DOI":"10.1109\/ICCV48922.2021.00081"},{"key":"7119_CR44","doi-asserted-by":"publisher","first-page":"34405","DOI":"10.5555\/3600270.3602763","volume":"35","author":"M Chantry","year":"2022","unstructured":"Chantry M, Vyas S, Palangi H, Rawat Y, Vineet V (2022) Robustness analysis of video-language models against visual and language perturbations. Adv Neural Inf Process Syst 35:34405\u201334420. https:\/\/doi.org\/10.5555\/3600270.3602763","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07119-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07119-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07119-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T03:14:00Z","timestamp":1741749240000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07119-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,12]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["7119"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07119-8","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,12]]},"assertion":[{"value":"23 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2025","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"606"}}