{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T12:43:37Z","timestamp":1759495417995,"version":"build-2065373602"},"publisher-location":"Berlin, Heidelberg","reference-count":39,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"value":"9783662722428","type":"print"},{"value":"9783662722435","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-662-72243-5_9","type":"book-chapter","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T12:15:04Z","timestamp":1759493704000},"page":"147-164","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ChordPrompt: Orchestrating Cross-Modal Prompt Synergy for\u00a0Multi-domain Incremental Learning in\u00a0CLIP"],"prefix":"10.1007","author":[{"given":"Zhiyuan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Bokui","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"9_CR1","first-page":"23716","volume":"35","author":"JB Alayrac","year":"2022","unstructured":"Alayrac, J.B., et al.: Flamingo: a visual language model for few-shot learning. Adv. Neural. Inf. Process. Syst. 35, 23716\u201323736 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Dhar, P., Singh, R.V., Peng, K.C., Wu, Z., Chellappa, R.: Learning without memorizing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5138\u20135146 (2019)","DOI":"10.1109\/CVPR.2019.00528"},{"key":"9_CR3","unstructured":"Ding, Y., Liu, L., Tian, C., Yang, J., Ding, H.: Don\u2019t stop learning: towards continual learning for the CLIP model. arXiv preprint arXiv:2207.09248 (2022)"},{"key":"9_CR4","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)"},{"key":"9_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-030-58565-5_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Douillard","year":"2020","unstructured":"Douillard, A., Cord, M., Ollion, C., Robert, T., Valle, E.: PODNet: pooled outputs distillation for small-tasks incremental learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 86\u2013102. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_6"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Douillard, A., Ram\u00e9, A., Couairon, G., Cord, M.: DyTox: transformers for continual learning with dynamic token expansion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9285\u20139295 (2022)","DOI":"10.1109\/CVPR52688.2022.00907"},{"issue":"4","key":"9_CR7","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","volume":"3","author":"RM French","year":"1999","unstructured":"French, R.M.: Catastrophic forgetting in connectionist networks. Trends Cogn. Sci. 3(4), 128\u2013135 (1999)","journal-title":"Trends Cogn. Sci."},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., et\u00a0al.: The many faces of robustness: a critical analysis of out-of-distribution generalization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8340\u20138349 (2021)","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"9_CR9","unstructured":"Jeeveswaran, K., Bhat, P.S., Zonooz, B., Arani, E.: BiRT: bio-inspired replay in vision transformers for continual learning. In: International Conference on Machine Learning, ICML 2023, pp. 14817\u201314835. PMLR (2023). https:\/\/proceedings.mlr.press\/v202\/jeeveswaran23a.html"},{"key":"9_CR10","unstructured":"Jia, C., et al.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International Conference on Machine Learning, pp. 4904\u20134916. PMLR (2021)"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Jung, D., Han, D., Bang, J., Song, H.: Generating instance-level prompts for rehearsal-free continual learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11847\u201311857 (2023)","DOI":"10.1109\/ICCV51070.2023.01088"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Khattak, M.U., Rasheed, H., Maaz, M., Khan, S., Khan, F.S.: MaPLe: multi-modal prompt learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19113\u201319122 (2023)","DOI":"10.1109\/CVPR52729.2023.01832"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3045\u20133059 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"9_CR14","unstructured":"Li, J., Li, D., Savarese, S., Hoi, S.: BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In: International Conference on Machine Learning, pp. 19730\u201319742. PMLR (2023)"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, pp. 4582\u20134597 (2021)","DOI":"10.18653\/v1\/2021.acl-long.353"},{"issue":"12","key":"9_CR16","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935\u20132947 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR17","unstructured":"Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"9_CR18","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2018)"},{"key":"9_CR19","unstructured":"Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"9_CR20","unstructured":"Ni, Z., Wei, L., Tang, S., Zhuang, Y., Tian, Q.: Continual vision-language representation learning with off-diagonal information. In: International Conference on Machine Learning, ICML 2023, pp. 26129\u201326149. PMLR (2023). https:\/\/proceedings.mlr.press\/v202\/ni23c.html"},{"key":"9_CR21","unstructured":"Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"9_CR22","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2001\u20132010 (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"9_CR24","unstructured":"Rusu, A.A., et al.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016)"},{"issue":"11","key":"9_CR25","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.tics.2008.07.006","volume":"12","author":"L Shams","year":"2008","unstructured":"Shams, L., Seitz, A.R.: Benefits of multisensory learning. Trends Cogn. Sci. 12(11), 411\u2013417 (2008)","journal-title":"Trends Cogn. Sci."},{"key":"9_CR26","unstructured":"Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"9_CR27","unstructured":"Thengane, V., Khan, S., Hayat, M., Khan, F.: CLIP model is an efficient continual learner. arXiv preprint arXiv:2210.03114 (2022)"},{"key":"9_CR28","unstructured":"Van\u00a0de Ven, G.M., Tolias, A.S.: Three scenarios for continual learning. arXiv preprint arXiv:1904.07734 (2019)"},{"key":"9_CR29","first-page":"5682","volume":"35","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Huang, Z., Hong, X.: S-prompts learning with pre-trained transformers: an Occam\u2019s Razor for domain incremental learning. Adv. Neural. Inf. Process. Syst. 35, 5682\u20135695 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/978-3-031-19809-0_36","volume-title":"Computer Vision \u2013 ECCV 2022","author":"Z Wang","year":"2022","unstructured":"Wang, Z., et al.: DualPrompt: complementary prompting for rehearsal-free continual learning. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13686, pp. 631\u2013648. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19809-0_36"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Learning to prompt for continual learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 139\u2013149 (2022)","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Wortsman, M., et\u00a0al.: Robust fine-tuning of zero-shot models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7959\u20137971 (2022)","DOI":"10.1109\/CVPR52688.2022.00780"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Xing, Y., et al.: Dual modality prompt tuning for vision-language pre-trained model. IEEE Trans. Multimed. (2023)","DOI":"10.1109\/TMM.2023.3291588"},{"key":"9_CR34","unstructured":"Yoon, J., Yang, E., Lee, J., Hwang, S.J.: Lifelong learning with dynamically expandable networks. In: 6th International Conference on Learning Representations, ICLR (2018)"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Yu, J., et al.: Boosting continual learning of vision-language models via mixture-of-experts adapters. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23219\u201323230 (2024)","DOI":"10.1109\/CVPR52733.2024.02191"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ma, M., Wang, K., Qin, Z., Yue, X., You, Y.: Preventing zero-shot transfer degradation in continual learning of vision-language models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 19125\u201319136 (2023)","DOI":"10.1109\/ICCV51070.2023.01752"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16816\u201316825 (2022)","DOI":"10.1109\/CVPR52688.2022.01631"},{"issue":"9","key":"9_CR38","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Int. J. Comput. Vision 130(9), 2337\u20132348 (2022)","journal-title":"Int. J. Comput. Vision"},{"key":"9_CR39","doi-asserted-by":"crossref","unstructured":"Zhu, J., Lai, S., Chen, X., Wang, D., Lu, H.: Visual prompt multi-modal tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9516\u20139526 (2023)","DOI":"10.1109\/CVPR52729.2023.00918"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-662-72243-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T12:15:33Z","timestamp":1759493733000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-662-72243-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"ISBN":["9783662722428","9783662722435"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-662-72243-5_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]},"assertion":[{"value":"4 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}