{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:40:06Z","timestamp":1746578406799,"version":"3.40.5"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62166042"],"award-info":[{"award-number":["62166042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100015310","name":"Natural Science Foundation of Xinjiang","doi-asserted-by":"publisher","award":["2021D01C076"],"award-info":[{"award-number":["2021D01C076"]}],"id":[{"id":"10.13039\/501100015310","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07273-z","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T12:36:50Z","timestamp":1746535010000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Domain-adaptive transfer network for visual\u2013textual cross-domain sentiment classification"],"prefix":"10.1007","volume":"81","author":[{"given":"Yuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Turdi","family":"Tohti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongfang","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zicheng","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingwen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Askar","family":"Hamdulla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"key":"7273_CR1","doi-asserted-by":"crossref","unstructured":"Luo L, Ao X, Pan F, Wang J, Zhao T, Yu N, He Q (2018) Beyond polarity: interpretable financial sentiment analysis with hierarchical query-driven attention. In: IJCAI, pp 4244\u20134250","DOI":"10.24963\/ijcai.2018\/590"},{"key":"7273_CR2","doi-asserted-by":"crossref","unstructured":"Preo\u0163iuc-Pietro D, Liu Y, Hopkins D, Ungar L (2017) Beyond binary labels: political ideology prediction of twitter users. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (volume 1: Long Papers), pp 729\u2013740","DOI":"10.18653\/v1\/P17-1068"},{"issue":"2","key":"7273_CR3","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1007\/s11227-023-05558-9","volume":"80","author":"Y Wang","year":"2024","unstructured":"Wang Y, Feng L, Liu A, Wang W, Hou Y (2024) Dual BiGRU-CNN-based sentiment classification method combining global and local attention. J Supercomput 80(2):2799\u20132837","journal-title":"J Supercomput"},{"key":"7273_CR4","doi-asserted-by":"crossref","unstructured":"Ma Z, Zheng Z, Ye J, Li J, Gao Z, Zhang S, Chen X (2023) emotion2vec: self-supervised pre-training for speech emotion representation. arXiv preprint arXiv:2312.15185","DOI":"10.18653\/v1\/2024.findings-acl.931"},{"issue":"1\u20132","key":"7273_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/1500000011","volume":"2","author":"B Pang","year":"2008","unstructured":"Pang B, Lee L et al (2008) Opinion mining and sentiment analysis. Found Trends Inf retrieval 2(1\u20132):1\u2013135","journal-title":"Found Trends Inf retrieval"},{"issue":"6","key":"7273_CR6","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1002\/widm.1171","volume":"5","author":"D Tang","year":"2015","unstructured":"Tang D, Qin B, Liu T (2015) Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdiscip Rev Data Mining Knowl Discov 5(6):292\u2013303","journal-title":"Wiley Interdiscip Rev Data Mining Knowl Discov"},{"key":"7273_CR7","doi-asserted-by":"crossref","unstructured":"Chen F, Gao Y, Cao D, Ji R (2015) Multimodal hypergraph learning for microblog sentiment prediction. In: 2015 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp 1\u20136","DOI":"10.1109\/ICME.2015.7177477"},{"key":"7273_CR8","doi-asserted-by":"crossref","unstructured":"Li L, Cao D, Li S, Ji R (2015) Sentiment analysis of Chinese micro-blog based on multi-modal correlation model. In: 2015 IEEE International Conference on Image Processing (ICIP). IEEE, pp 4798\u20134802","DOI":"10.1109\/ICIP.2015.7351718"},{"issue":"17","key":"7273_CR9","doi-asserted-by":"publisher","first-page":"25563","DOI":"10.1007\/s11227-024-06422-0","volume":"80","author":"B Yu","year":"2024","unstructured":"Yu B, Shi Z (2024) TEMM: text-enhanced multi-interactive attention and multitask learning network for multimodal sentiment analysis. J Supercomput 80(17):25563\u201325589","journal-title":"J Supercomput"},{"issue":"8","key":"7273_CR10","doi-asserted-by":"publisher","first-page":"8611","DOI":"10.1007\/s11227-022-05001-5","volume":"79","author":"Y Yi","year":"2023","unstructured":"Yi Y, Tian Y, He C, Fan Y, Hu X, Xu Y (2023) DBT: multimodal emotion recognition based on dual-branch transformer. J Supercomput 79(8):8611\u20138633","journal-title":"J Supercomput"},{"issue":"2","key":"7273_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103223","volume":"60","author":"Z Zhu","year":"2023","unstructured":"Zhu Z, Zhang D, Li L, Li K, Qi J, Wang W, Zhang G, Liu P (2023) Knowledge-guided multi-granularity GCN for ABSA. Inf Process Manag 60(2):103223","journal-title":"Inf Process Manag"},{"issue":"2","key":"7273_CR12","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1109\/TNNLS.2020.3028503","volume":"33","author":"S Zhao","year":"2020","unstructured":"Zhao S, Yue X, Zhang S, Li B, Zhao H, Wu B, Krishna R, Gonzalez JE, Sangiovanni-Vincentelli AL, Seshia SA et al (2020) A review of single-source deep unsupervised visual domain adaptation. IEEE Trans Neural Netw Learn Syst 33(2):473\u2013493","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"7273_CR13","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TKDE.2017.2756658","volume":"30","author":"W Zhao","year":"2017","unstructured":"Zhao W, Guan Z, Chen L, He X, Cai D, Wang B, Wang Q (2017) Weakly-supervised deep embedding for product review sentiment analysis. IEEE Trans Knowl Data Eng 30(1):185\u2013197","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7273_CR14","doi-asserted-by":"crossref","unstructured":"Peng M, Zhang Q, Jiang Y-g, Huang X-J (2018) Cross-domain sentiment classification with target domain specific information. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 2505\u20132513","DOI":"10.18653\/v1\/P18-1233"},{"issue":"5","key":"7273_CR15","doi-asserted-by":"publisher","first-page":"162","DOI":"10.3390\/info10050162","volume":"10","author":"J Meng","year":"2019","unstructured":"Meng J, Long Y, Yu Y, Zhao D, Liu S (2019) Cross-domain text sentiment analysis based on CNN_FT method. Information 10(5):162","journal-title":"Information"},{"key":"7273_CR16","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7167\u20137176","DOI":"10.1109\/CVPR.2017.316"},{"key":"7273_CR17","doi-asserted-by":"crossref","unstructured":"Poria S, Hazarika D, Majumder N, Naik G, Cambria E, Mihalcea R (2018) MELD: a multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508","DOI":"10.18653\/v1\/P19-1050"},{"key":"7273_CR18","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10579-008-9076-6","volume":"42","author":"C Busso","year":"2008","unstructured":"Busso C, Bulut M, Lee C-C, Kazemzadeh A, Mower E, Kim S, Chang JN, Lee S, Narayanan SS (2008) IEMOCAP: interactive emotional dyadic motion capture database. Lang Resour Eval 42:335\u2013359","journal-title":"Lang Resour Eval"},{"key":"7273_CR19","first-page":"305","volume":"33","author":"Q-T Truong","year":"2019","unstructured":"Truong Q-T, Lauw HW (2019) VistaNet: visual aspect attention network for multimodal sentiment analysis. Proc AAAI Conf Artif Intell 33:305\u2013312","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7273_CR20","doi-asserted-by":"crossref","unstructured":"Niu T, Zhu S, Pang L, El\u00a0Saddik A (2016) Sentiment analysis on multi-view social data. In: MultiMedia Modeling: 22nd International Conference, MMM 2016, Miami, FL, USA, January 4\u20136, 2016, Proceedings, Part II 22. Springer, pp 15\u201327","DOI":"10.1007\/978-3-319-27674-8_2"},{"key":"7273_CR21","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.inffus.2023.02.028","volume":"95","author":"L Zhu","year":"2023","unstructured":"Zhu L, Zhu Z, Zhang C, Xu Y, Kong X (2023) Multimodal sentiment analysis based on fusion methods: a survey. Inf Fusion 95:306\u2013325","journal-title":"Inf Fusion"},{"key":"7273_CR22","unstructured":"Vaswani A (2017) Attention is all you need. In: Advances in Neural Information Processing Systems"},{"issue":"140","key":"7273_CR23","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(140):1\u201367","journal-title":"J Mach Learn Res"},{"key":"7273_CR24","first-page":"1","volume":"61","author":"Y Zhan","year":"2023","unstructured":"Zhan Y, Xiong Z, Yuan Y (2023) RSVG: exploring data and models for visual grounding on remote sensing data. IEEE Trans Geosci Remote Sens 61:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7273_CR25","first-page":"6988","volume":"38","author":"Y Zhan","year":"2024","unstructured":"Zhan Y, Yuan Y, Xiong Z (2024) Mono3DVG: 3D visual grounding in monocular images. Proc AAAI Conf Artif Intell 38:6988\u20136996","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"7273_CR26","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M-A, Lacroix T, Rozi\u00e8re B, Goyal N, Hambro E, Azhar F et al. (2023) Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971"},{"key":"7273_CR27","unstructured":"Gu A, Dao T (2023) Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752"},{"key":"7273_CR28","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.isprsjprs.2025.01.020","volume":"221","author":"Y Zhan","year":"2025","unstructured":"Zhan Y, Xiong Z, Yuan Y (2025) SkyEyeGPT: unifying remote sensing vision-language tasks via instruction tuning with large language model. ISPRS J Photogramm Remote Sens 221:64\u201377","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"7273_CR29","unstructured":"Tan M (2019) EfficientNet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946"},{"key":"7273_CR30","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J et al (2021) Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. PMLR, pp 8748\u20138763"},{"key":"7273_CR31","first-page":"1","volume":"61","author":"Y Yuan","year":"2023","unstructured":"Yuan Y, Zhan Y, Xiong Z (2023) Parameter-efficient transfer learning for remote sensing image-text retrieval. IEEE Trans Geosci Remote Sens 61:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7273_CR32","unstructured":"GPT-4V(ision) system card. (2023). https:\/\/api.semanticscholar.org\/CorpusID:263218031"},{"key":"7273_CR33","unstructured":"Li J, Li D, Savarese S, Hoi S (2023) Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In: International Conference on Machine Learning. PMLR, pp 19730\u201319742"},{"key":"7273_CR34","doi-asserted-by":"crossref","unstructured":"Liu H, Li C, Wu Q, Lee YJ (2024) Visual instruction tuning. In: Advances in Neural Information Processing Systems 36","DOI":"10.1007\/978-981-99-8079-6_1"},{"key":"7273_CR35","doi-asserted-by":"crossref","unstructured":"Liang PP, Liu Z, Zadeh A, Morency L-P (2018) Multimodal language analysis with recurrent multistage fusion. arXiv preprint arXiv:1808.03920","DOI":"10.18653\/v1\/D18-1014"},{"key":"7273_CR36","first-page":"7216","volume":"33","author":"Y Wang","year":"2019","unstructured":"Wang Y, Shen Y, Liu Z, Liang PP, Zadeh A, Morency L-P (2019) Words can shift: dynamically adjusting word representations using nonverbal behaviors. Proc AAAI Conf Artif Intell 33:7216\u20137223","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"6","key":"7273_CR37","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1109\/TNNLS.2020.3006531","volume":"32","author":"L Zhu","year":"2020","unstructured":"Zhu L, Li W, Shi Y, Guo K (2020) SentiVec: learning sentiment-context vector via kernel optimization function for sentiment analysis. IEEE Trans Neural Netw Learn Syst 32(6):2561\u20132572","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7273_CR38","doi-asserted-by":"crossref","unstructured":"Mai S, Hu H, Xing S (2019) Divide, conquer and combine: hierarchical feature fusion network with local and global perspectives for multimodal affective computing. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 481\u2013492","DOI":"10.18653\/v1\/P19-1046"},{"key":"7273_CR39","doi-asserted-by":"crossref","unstructured":"Han W, Chen H, Poria S (2021) Improving multimodal fusion with hierarchical mutual information maximization for multimodal sentiment analysis. arXiv preprint arXiv:2109.00412","DOI":"10.18653\/v1\/2021.emnlp-main.723"},{"key":"7273_CR40","doi-asserted-by":"crossref","unstructured":"Hu G, Lin T-E, Zhao Y, Lu G, Wu Y, Li Y (2022) UniMSE: towards unified multimodal sentiment analysis and emotion recognition. arXiv preprint arXiv:2211.11256","DOI":"10.18653\/v1\/2022.emnlp-main.534"},{"key":"7273_CR41","doi-asserted-by":"crossref","unstructured":"Tsai Y-HH, Bai S, Liang PP, Kolter JZ, Morency L-P, Salakhutdinov R (2019) Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the Conference Association for Computational Linguistics Meeting, vol. 2019. NIH Public Access, p 6558","DOI":"10.18653\/v1\/P19-1656"},{"key":"7273_CR42","doi-asserted-by":"crossref","unstructured":"Li M, Huang S-L, Zhang L (2021) OTCMR: bridging heterogeneity gap with optimal transport for cross-modal retrieval. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp 3216\u20133220","DOI":"10.1145\/3459637.3482158"},{"key":"7273_CR43","doi-asserted-by":"crossref","unstructured":"Basu P, Tiwari S, Mohanty J, Karmakar S (2020) Multimodal sentiment analysis of# metoo tweets using focal loss (grand challenge). In: 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM). IEEE, pp 461\u2013465","DOI":"10.1109\/BigMM50055.2020.00076"},{"key":"7273_CR44","doi-asserted-by":"crossref","unstructured":"Thuseethan S, Janarthan S, Rajasegarar S, Kumari P, Yearwood J (2020) Multimodal deep learning framework for sentiment analysis from text-image web data. In: 2020 IEEE\/WIC\/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, pp 267\u2013274","DOI":"10.1109\/WIIAT50758.2020.00039"},{"key":"7273_CR45","doi-asserted-by":"crossref","unstructured":"Gui T, Zhu L, Zhang Q, Peng M, Zhou X, Ding K, Chen Z (2019) Cooperative multimodal approach to depression detection in twitter. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp 110\u2013117","DOI":"10.1609\/aaai.v33i01.3301110"},{"key":"7273_CR46","doi-asserted-by":"crossref","unstructured":"Du C, Sun H, Wang J, Qi Q, Liao J (2020) Adversarial and domain-aware BERT for cross-domain sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 4019\u20134028","DOI":"10.18653\/v1\/2020.acl-main.370"},{"key":"7273_CR47","unstructured":"Arbel M, Korba A, Salim A, Gretton A (2019) Maximum mean discrepancy gradient flow. In: Advances in Neural Information Processing Systems 32"},{"issue":"59","key":"7273_CR48","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, March M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(59):1\u201335","journal-title":"J Mach Learn Res"},{"key":"7273_CR49","doi-asserted-by":"crossref","unstructured":"Ye H, Tan Q, He R, Li J, Ng HT, Bing L (2020) Feature adaptation of pre-trained language models across languages and domains with robust self-training. arXiv preprint arXiv:2009.11538","DOI":"10.18653\/v1\/2020.emnlp-main.599"},{"key":"7273_CR50","doi-asserted-by":"crossref","unstructured":"Wang Q, Breckon T (2020) Unsupervised domain adaptation via structured prediction based selective pseudo-labeling. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp 6243\u20136250","DOI":"10.1609\/aaai.v34i04.6091"},{"key":"7273_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120223","volume":"662","author":"C Zhu","year":"2024","unstructured":"Zhu C, Wang Q, Xie Y, Xu S (2024) Multiview latent space learning with progressively fine-tuned deep features for unsupervised domain adaptation. Inf Sci 662:120223","journal-title":"Inf Sci"},{"key":"7273_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106859","volume":"181","author":"C Zhu","year":"2025","unstructured":"Zhu C, Zhang L, Luo W, Jiang G, Wang Q (2025) Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation. Neural Netw 181:106859","journal-title":"Neural Netw"},{"key":"7273_CR53","doi-asserted-by":"crossref","unstructured":"Gong C, Yu J, Xia R (2020) Unified feature and instance based domain adaptation for aspect-based sentiment analysis. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 7035\u20137045","DOI":"10.18653\/v1\/2020.emnlp-main.572"},{"key":"7273_CR54","doi-asserted-by":"publisher","first-page":"3449","DOI":"10.1109\/TIP.2022.3169689","volume":"31","author":"K Gao","year":"2022","unstructured":"Gao K, Liu B, Yu X, Yu A (2022) Unsupervised meta learning with multiview constraints for hyperspectral image small sample set classification. IEEE Trans Image Process 31:3449\u20133462","journal-title":"IEEE Trans Image Process"},{"key":"7273_CR55","first-page":"1","volume":"62","author":"K Gao","year":"2023","unstructured":"Gao K, Yu A, You X, Guo W, Li K, Huang N (2023) Integrating multiple sources knowledge for class asymmetry domain adaptation segmentation of remote sensing images. IEEE Trans Geosci Remote Sens 62:1\u201318","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"11","key":"7273_CR56","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"issue":"6","key":"7273_CR57","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1093\/bib\/bbad329","volume":"24","author":"M Shi","year":"2023","unstructured":"Shi M, Li X, Li M, Si Y (2023) Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data. Brief Bioinform 24(6):329","journal-title":"Brief Bioinform"},{"issue":"9","key":"7273_CR58","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.1109\/TMM.2016.2582379","volume":"18","author":"X Yang","year":"2016","unstructured":"Yang X, Zhang T, Xu C, Yan S, Hossain MS, Ghoneim A (2016) Deep relative attributes. IEEE Trans Multimedia 18(9):1832\u20131842","journal-title":"IEEE Trans Multimedia"},{"issue":"9","key":"7273_CR59","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1109\/TMM.2019.2902100","volume":"21","author":"X Ma","year":"2019","unstructured":"Ma X, Zhang T, Xu C (2019) Deep multi-modality adversarial networks for unsupervised domain adaptation. IEEE Trans Multimedia 21(9):2419\u20132431","journal-title":"IEEE Trans Multimedia"},{"key":"7273_CR60","doi-asserted-by":"crossref","unstructured":"Dong H, Chatzi E, Fink O (2024) Towards multimodal open-set domain generalization and adaptation through self-supervision. In: European Conference on Computer Vision. Springer, pp 270\u2013287","DOI":"10.1007\/978-3-031-73202-7_16"},{"key":"7273_CR61","doi-asserted-by":"crossref","unstructured":"Tang S, Su W, Ye M, Zhu X (2024) Source-free domain adaptation with frozen multimodal foundation model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 23711\u201323720","DOI":"10.1109\/CVPR52733.2024.02238"},{"key":"7273_CR62","doi-asserted-by":"crossref","unstructured":"Al\u00a0Adel A, Burtsev MS (2021) Memory transformer with hierarchical attention for long document processing. In: 2021 International Conference Engineering and Telecommunication (En &T). IEEE, pp 1\u20137","DOI":"10.1109\/EnT50460.2021.9681776"},{"key":"7273_CR63","doi-asserted-by":"crossref","unstructured":"Xiao X, Pu Y, Zhao Z, Gu J, Xu D (2023) Bit: Improving image-text sentiment analysis via learning bidirectional image-text interaction. In: 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1\u20139","DOI":"10.1109\/IJCNN54540.2023.10191445"},{"key":"7273_CR64","unstructured":"Dosovitskiy A (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"7273_CR65","unstructured":"Devlin J (2018) BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"7273_CR66","doi-asserted-by":"crossref","unstructured":"Hu H, Gu J, Zhang Z, Dai J, Wei Y (2018) Relation networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3588\u20133597","DOI":"10.1109\/CVPR.2018.00378"},{"key":"7273_CR67","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 27"},{"issue":"10","key":"7273_CR68","doi-asserted-by":"publisher","first-page":"7956","DOI":"10.1109\/TNNLS.2022.3147546","volume":"34","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Zhang Y, Guo W, Cai X, Yuan X (2022) Learning disentangled representation for multimodal cross-domain sentiment analysis. IEEE Trans Neural Netw Learn Syst 34(10):7956\u20137966","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7273_CR69","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1109\/TASLP.2021.3049898","volume":"29","author":"Z Lian","year":"2021","unstructured":"Lian Z, Liu B, Tao J (2021) CTNet: conversational transformer network for emotion recognition. IEEE\/ACM Trans Audio Speech Lang Process 29:985\u20131000","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"7273_CR70","doi-asserted-by":"crossref","unstructured":"Qi F, Yang X, Xu C (2018) A unified framework for multimodal domain adaptation. In: Proceedings of the 26th ACM International Conference on Multimedia, pp 429\u2013437","DOI":"10.1145\/3240508.3240633"},{"key":"7273_CR71","doi-asserted-by":"crossref","unstructured":"Li J, Li G, Shi Y, Yu Y (2021) Cross-domain adaptive clustering for semi-supervised domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2505\u20132514","DOI":"10.1109\/CVPR46437.2021.00253"},{"key":"7273_CR72","doi-asserted-by":"crossref","unstructured":"Yu Y-C, Lin H-T (2023) Semi-supervised domain adaptation with source label adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 24100\u201324109","DOI":"10.1109\/CVPR52729.2023.02308"},{"key":"7273_CR73","doi-asserted-by":"crossref","unstructured":"Westfechtel T, Yeh H-W, Zhang D, Harada T (2024) Gradual source domain expansion for unsupervised domain adaptation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 1946\u20131955","DOI":"10.1109\/WACV57701.2024.00195"},{"issue":"11","key":"7273_CR74","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07273-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07273-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07273-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:02:34Z","timestamp":1746576154000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07273-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,6]]},"references-count":74,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["7273"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07273-z","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,6]]},"assertion":[{"value":"30 March 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 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"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This manuscript has not been published nor is it currently under consideration for publication elsewhere.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"831"}}