{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:17:57Z","timestamp":1772554677293,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T00:00:00Z","timestamp":1737417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T00:00:00Z","timestamp":1737417600000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s13042-025-02549-5","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T05:11:05Z","timestamp":1737436265000},"page":"4899-4911","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive multimodal prompt-tuning model for few-shot multimodal sentiment analysis"],"prefix":"10.1007","volume":"16","author":[{"given":"Yan","family":"Xiang","sequence":"first","affiliation":[]},{"given":"Anlan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Junjun","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yuxin","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"key":"2549_CR1","doi-asserted-by":"publisher","unstructured":"Hou Z, Guo J (2024) Virtual visual-guided domain-shadow fusion via modal exchanging for domain-specific multi-modal neural machine translation. In: Proceedings of the 32nd ACM international conference on multimedia (MM '24). Association for Computing Machinery, New York, NY, USA, pp 4227\u20134235. https:\/\/doi.org\/10.1145\/3664647.3681525","DOI":"10.1145\/3664647.3681525"},{"key":"2549_CR2","doi-asserted-by":"crossref","unstructured":"Zhang D, Li S, Zhu Q, Zhou G (2019) Effective sentiment-relevant word selection for multi-modal sentiment analysis in spoken language. In: Proceedings of the 27th ACM International Conference on Multimedia, pp 148\u2013156","DOI":"10.1145\/3343031.3350987"},{"key":"2549_CR3","doi-asserted-by":"crossref","unstructured":"Yu Y, Zhang D, Li S (2022) Unified multi-modal pre-training for few-shot sentiment analysis with prompt-based learning. In: MM \u201922: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10\u201314, 2022 189\u2013198","DOI":"10.1145\/3503161.3548306"},{"key":"2549_CR4","doi-asserted-by":"publisher","unstructured":"Gao T, Fisch A, Chen D (2021) Making pre-trained language models better few-shot learners. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1\u20136, 2021, pp 3816\u20133830 https:\/\/doi.org\/10.18653\/V1\/2021.ACL-LONG.295","DOI":"10.18653\/V1\/2021.ACL-LONG.295"},{"key":"2549_CR5","doi-asserted-by":"publisher","unstructured":"Shin T, Razeghi Y, IV RLL, Wallace E, Singh S (2020) Autoprompt: Eliciting knowledge from language models with automatically generated prompts. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16\u201320, 2020, pp 4222\u20134235 https:\/\/doi.org\/10.18653\/V1\/2020.EMNLP-MAIN.346","DOI":"10.18653\/V1\/2020.EMNLP-MAIN.346"},{"key":"2549_CR6","doi-asserted-by":"publisher","unstructured":"Yu Y, Zhang D (2022) Few-shot multi-modal sentiment analysis with prompt-based vision-aware language modeling. In: IEEE International Conference on Multimedia and Expo, ICME 2022, Taipei, Taiwan, July 18-22, 2022, pp 1\u20136 https:\/\/doi.org\/10.1109\/ICME52920.2022.9859654","DOI":"10.1109\/ICME52920.2022.9859654"},{"key":"2549_CR7","doi-asserted-by":"publisher","unstructured":"Tan H, Li J, Zhou Y, Wan J, Lei Z, Zhang X (2024) Compound text-guided prompt tuning via image-adaptive cues. In: Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, February 20-27, 2024, Vancouver, Canada, pp 5061\u20135069 https:\/\/doi.org\/10.1609\/AAAI.V38I5.28311","DOI":"10.1609\/AAAI.V38I5.28311"},{"key":"2549_CR8","doi-asserted-by":"publisher","unstructured":"Jia M, Tang L, Chen B, Cardie C, Belongie SJ, Hariharan B, Lim S (2022) Visual prompt tuning. In: Computer Vision\u2013ECCV 2022\u201317th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXIII. Lecture Notes in Computer Science, vol. 13693, pp 709\u2013727 . https:\/\/doi.org\/10.1007\/978-3-031-19827-4_41","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"2549_CR9","doi-asserted-by":"publisher","unstructured":"Xu N, Mao W, Chen G (2018) A co-memory network for multimodal sentiment analysis. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018, pp 929\u2013932 https:\/\/doi.org\/10.1145\/3209978.3210093","DOI":"10.1145\/3209978.3210093"},{"key":"2549_CR10","doi-asserted-by":"publisher","unstructured":"Yang X, Feng S, Wang D, Zhang Y (2021) Image-text multimodal emotion classification via multi-view attentional network. IEEE Trans. Multim. 23, 4014\u20134026 https:\/\/doi.org\/10.1109\/TMM.2020.3035277","DOI":"10.1109\/TMM.2020.3035277"},{"key":"2549_CR11","doi-asserted-by":"crossref","unstructured":"Babichev SA, Ries J, Lvovsky AI (2002) Quantum scissors: teleportation of single-mode optical states by means of a nonlocal single photon. Preprint at https:\/\/arxiv.org\/abs\/quant-ph\/0208066v1","DOI":"10.1209\/epl\/i2003-00504-y"},{"key":"2549_CR12","doi-asserted-by":"publisher","unstructured":"Xu N, Mao W, Chen G (2019) Multi-interactive memory network for aspect based multimodal sentiment analysis. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27\u2013February 1, pp 371\u2013378 (2019). https:\/\/doi.org\/10.1609\/AAAI.V33I01.3301371","DOI":"10.1609\/AAAI.V33I01.3301371"},{"key":"2549_CR13","doi-asserted-by":"publisher","unstructured":"Khan Z, Fu Y (2021) Exploiting BERT for multimodal target sentiment classification through input space translation. In: MM \u201921: ACM Multimedia Conference, Virtual Event, China, October 20\u201324, 2021, pp 3034\u20133042 https:\/\/doi.org\/10.1145\/3474085.3475692","DOI":"10.1145\/3474085.3475692"},{"key":"2549_CR14","doi-asserted-by":"publisher","unstructured":"Yu J, Wang J, Xia R, Li J (2022) Targeted multimodal sentiment classification based on coarse-to-fine grained image-target matching. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pp 4482\u20134488 https:\/\/doi.org\/10.24963\/IJCAI.2022\/622","DOI":"10.24963\/IJCAI.2022\/622"},{"key":"2549_CR15","unstructured":"Zhao F, Wu Z, Long S, Dai X, Huang S, Chen J (2022) Learning from adjective-noun pairs: A knowledge-enhanced framework for target-oriented multimodal sentiment classification. In: Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, October 12-17, 2022, pp 6784\u20136794"},{"key":"2549_CR16","doi-asserted-by":"publisher","unstructured":"Yang H, Zhao Y, Qin B (2022) Face-sensitive image-to-emotional-text cross-modal translation for multimodal aspect-based sentiment analysis. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pp 3324\u20133335 https:\/\/doi.org\/10.18653\/V1\/2022.EMNLP-MAIN.219","DOI":"10.18653\/V1\/2022.EMNLP-MAIN.219"},{"issue":"6","key":"2549_CR17","doi-asserted-by":"publisher","first-page":"7259","DOI":"10.1007\/s11063-023-11259-4","volume":"55","author":"P Zhang","year":"2023","unstructured":"Zhang P, Chai T, Xu Y (2023) Adaptive prompt learning-based few-shot sentiment analysis. Neural Processing Letters 55(6):7259\u20137272","journal-title":"Neural Processing Letters"},{"key":"2549_CR18","doi-asserted-by":"publisher","unstructured":"Hu S, Ding N, Wang H, Liu Z, Wang J, Li J, Wu W, Sun M (2022) Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pp 2225\u20132240 https:\/\/doi.org\/10.18653\/V1\/2022.ACL-LONG.158","DOI":"10.18653\/V1\/2022.ACL-LONG.158"},{"key":"2549_CR19","unstructured":"Feng J, Lin M, Shang L, Gao X (2024) Autonomous aspect-image instruction a2ii: Q-former guided multimodal sentiment classification. In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp 1996\u20132005"},{"key":"2549_CR20","doi-asserted-by":"publisher","unstructured":"Yang X, Feng S, Wang D, Zhang Y, Poria S (2023) Few-shot multimodal sentiment analysis based on multimodal probabilistic fusion prompts. In: Proceedings of the 31st ACM International Conference on Multimedia, MM 2023, Ottawa, ON, Canada, 29 October 2023- 3 November 2023, pp 6045\u20136053 https:\/\/doi.org\/10.1145\/3581783.3612181","DOI":"10.1145\/3581783.3612181"},{"key":"2549_CR21","unstructured":"Li J, Li D, Xiong C, Hoi SCH (2022) BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research, vol. 162, pp 12888\u201312900"},{"key":"2549_CR22","doi-asserted-by":"publisher","unstructured":"Liu D, Li L, Tao X, Cui J, Xie Q (2023) Descriptive prompt paraphrasing for target-oriented multimodal sentiment classification. In: Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023, pp 4174\u20134186 https:\/\/doi.org\/10.18653\/V1\/2023.FINDINGS-EMNLp275","DOI":"10.18653\/V1\/2023.FINDINGS-EMNLp275"},{"key":"2549_CR23","doi-asserted-by":"publisher","unstructured":"Wang Z, Yang B (2020) Attention-based bidirectional long short-term memory networks for relation classification using knowledge distillation from BERT. In: IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress, DASC\/PiCom\/CBDCom\/CyberSciTech 2020, Calgary, AB, Canada, August 17-22, 2020, pp 562\u2013568 https:\/\/doi.org\/10.1109\/DASC-PICOM-CBDCOM-CYBERSCITECH49142.2020.00100","DOI":"10.1109\/DASC-PICOM-CBDCOM-CYBERSCITECH49142.2020.00100"},{"key":"2549_CR24","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs\/1907.11692"},{"key":"2549_CR25","doi-asserted-by":"publisher","unstructured":"Yu J, Jiang J (2019) Adapting BERT for target-oriented multimodal sentiment classification. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pp 5408\u20135414 https:\/\/doi.org\/10.24963\/IJCAI.2019\/751","DOI":"10.24963\/IJCAI.2019\/751"},{"key":"2549_CR26","doi-asserted-by":"publisher","unstructured":"Niu T, Zhu S, Pang L, El-Saddik A (2016) Sentiment analysis on multi-view social data. In: MultiMedia Modeling\u201322nd International Conference, MMM 2016, Miami, FL, USA, January 4-6, 2016, Proceedings, Part II. Lecture Notes in Computer Science, vol. 9517, pp 15\u201327 https:\/\/doi.org\/10.1007\/978-3-319-27674-8_2","DOI":"10.1007\/978-3-319-27674-8_2"},{"key":"2549_CR27","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp 4171\u20134186 https:\/\/doi.org\/10.18653\/V1\/N19-1423","DOI":"10.18653\/V1\/N19-1423"},{"key":"2549_CR28","unstructured":"Liu Y (2019) Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692364"},{"key":"2549_CR29","doi-asserted-by":"publisher","unstructured":"Yang X, Feng S, Zhang Y, Wang D (2021) Multimodal sentiment detection based on multi-channel graph neural networks. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, pp 328\u2013339 https:\/\/doi.org\/10.18653\/V1\/2021.ACL-LONG.28","DOI":"10.18653\/V1\/2021.ACL-LONG.28"},{"key":"2549_CR30","doi-asserted-by":"publisher","unstructured":"Li Z, Xu B, Zhu C, Zhao T (2022) CLMLF: A contrastive learning and multi-layer fusion method for multimodal sentiment detection. In: Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, WA, United States, July 10-15, 2022, pp 2282\u20132294 https:\/\/doi.org\/10.18653\/V1\/2022.FINDINGS-NAACL.175","DOI":"10.18653\/V1\/2022.FINDINGS-NAACL.175"},{"key":"2549_CR31","unstructured":"Targ S, Almeida D, Lyman K (2016) Resnet in resnet: Generalizing residual architectures. CoRR abs\/1603.08029"},{"key":"2549_CR32","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 (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021"},{"key":"2549_CR33","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I (2021) Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp 8748\u20138763"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02549-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02549-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02549-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T03:44:20Z","timestamp":1757130260000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02549-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,21]]},"references-count":33,"journal-issue":{"issue":"7-8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2549"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02549-5","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,21]]},"assertion":[{"value":"11 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}