{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T07:00:44Z","timestamp":1771743644399,"version":"3.50.1"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049704","type":"print"},{"value":"9783032049711","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-04971-1_20","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T17:10:43Z","timestamp":1758301843000},"page":"208-218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Global and\u00a0Local Vision-Language Alignment for\u00a0Few-Shot Learning and\u00a0Few-Shot OOD Detection"],"prefix":"10.1007","author":[{"given":"Jie","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyuan","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Shi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruixuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"20_CR1","unstructured":"Bai, J., et\u00a0al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Bai, Y., Han, Z., Cao, B., Jiang, X., Hu, Q., Zhang, C.: Id-like prompt learning for few-shot out-of-distribution detection. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01655"},{"issue":"7","key":"20_CR3","doi-asserted-by":"publisher","first-page":"2488","DOI":"10.3390\/app10072488","volume":"10","author":"MN Bajwa","year":"2020","unstructured":"Bajwa, M.N., et al.: Computer-aided diagnosis of skin diseases using deep neural networks. Appl. Sci. 10(7), 2488 (2020)","journal-title":"Appl. Sci."},{"key":"20_CR4","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML (2020)"},{"key":"20_CR5","unstructured":"Chen, X., Li, Y., Chen, H.: Dual-adapter: Training-free dual adaptation for few-shot out-of-distribution detection. arXiv preprint arXiv:2405.16146 (2024)"},{"issue":"2","key":"20_CR6","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s11263-023-01891-x","volume":"132","author":"P Gao","year":"2024","unstructured":"Gao, P., et al.: Clip-adapter: better vision-language models with feature adapters. Int. J. Comput. Vision 132(2), 581\u2013595 (2024)","journal-title":"Int. J. Comput. Vision"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"20_CR8","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: ICLR (2017)"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Khattak, M.U., Rasheed, H., Maaz, M., Khan, S., Khan, F.S.: Maple: multi-modal prompt learning. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01832"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Khattak, M.U., Wasim, S.T., Naseer, M., Khan, S., Yang, M.H., Khan, F.S.: Self-regulating prompts: foundational model adaptation without forgetting. In: CVPR (2023)","DOI":"10.1109\/ICCV51070.2023.01394"},{"key":"20_CR11","unstructured":"Khosla, P., et al.: Supervised contrastive learning. NeurIPS (2020)"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Lafon, M., Ramzi, E., Rambour, C., Audebert, N., Thome, N.: Gallop: Learning global and local prompts for vision-language models. In: ECCV (2024)","DOI":"10.1007\/978-3-031-73030-6_15"},{"key":"20_CR13","unstructured":"Li, J., Li, H., Erfani, S.M., Feng, L., Bailey, J., Liu, F.: Visual-text cross alignment: refining the similarity score in vision-language models. In: ICML (2024)"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Li, T., Pang, G., Bai, X., Miao, W., Zheng, J.: Learning transferable negative prompts for out-of-distribution detection. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01665"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Lin, Y., Chen, Y., Cheng, K.T., Chen, H.: Few shot medical image segmentation with cross attention transformer. In: MICCAI (2023)","DOI":"10.1007\/978-3-031-43895-0_22"},{"issue":"10","key":"20_CR16","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1007\/s10462-024-10915-y","volume":"57","author":"F Liu","year":"2024","unstructured":"Liu, F., et al.: Few-shot adaptation of multi-modal foundation models: a survey. Artif. Intell. Rev. 57(10), 268 (2024)","journal-title":"Artif. Intell. Rev."},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Marimont, S.N., Siomos, V., Tarroni, G.: Mim-ood: generative masked image modelling for out-of-distribution detection in medical images. In: MICCAI (2023)","DOI":"10.1007\/978-3-031-53767-7_4"},{"key":"20_CR18","unstructured":"Ming, Y., Cai, Z., Gu, J., Sun, Y., Li, W., Li, Y.: Delving into out-of-distribution detection with vision-language representations. NeurIPS (2022)"},{"key":"20_CR19","unstructured":"Ming, Y., Sun, Y., Dia, O., Li, Y.: How to exploit hyperspherical embeddings for out-of-distribution detection? arXiv preprint arXiv:2203.04450 (2022)"},{"key":"20_CR20","unstructured":"Miyai, A., et\u00a0al.: Generalized out-of-distribution detection and beyond in vision language model era: a survey. arXiv preprint arXiv:2407.21794 (2024)"},{"key":"20_CR21","unstructured":"Miyai, A., Yu, Q., Irie, G., Aizawa, K.: Locoop: few-shot out-of-distribution detection via prompt learning. NeurIPS (2023)"},{"key":"20_CR22","unstructured":"Nie, J., Zhang, Y., Fang, Z., Liu, T., Han, B., Tian, X.: Out-of-distribution detection with negative prompts. In: ICLR (2024)"},{"issue":"2","key":"20_CR23","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/data6020014","volume":"6","author":"S Pachade","year":"2021","unstructured":"Pachade, S., et al.: etinal fundus multi-disease image dataset (rfmid): a dataset for multi-disease detection research. Data 6(2), 14 (2021)","journal-title":"Data"},{"key":"20_CR24","unstructured":"Parnami, A., Lee, M.: Learning from few examples: a summary of approaches to few-shot learning. arXiv preprint arXiv:2203.04291 (2022)"},{"key":"20_CR25","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: ICML (2021)"},{"key":"20_CR26","unstructured":"Runhe, L., Xinhua, L., Kanghao, C., Qichao, C., Wei-Shi, Z., Ruixuan, W.: Hierarchical vision-language learning for medical out-of-distribution detection. In: MICCAI (2025)"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Shakeri, F., et al.:Few-shot adaptation of medical vision-language models. In: MICCAI (2024)","DOI":"10.1007\/978-3-031-72390-2_52"},{"issue":"13s","key":"20_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582688","volume":"55","author":"Y Song","year":"2023","unstructured":"Song, Y., Wang, T., Cai, P., Mondal, S.K., Sahoo, J.P.: A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. ACM Comput. Surv. 55(13s), 1\u201340 (2023)","journal-title":"ACM Comput. Surv."},{"key":"20_CR29","unstructured":"Sun, H., He, R., Han, Z., Lin, Z., Gong, Y., Yin, Y.: Clip-driven outliers synthesis for few-shot ood detection. arXiv preprint arXiv:2404.00323 (2024)"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Sun, X., Yang, J., Sun, M., Wang, K.: A benchmark for automatic visual classification of clinical skin disease images. In: ECCV. pp. 206\u2013222. Springer (2016)","DOI":"10.1007\/978-3-319-46466-4_13"},{"key":"20_CR31","unstructured":"Tao, L., Du, X., Zhu, X., Li, Y.: Non-parametric outlier synthesis. arXiv preprint arXiv:2303.02966 (2023)"},{"issue":"1","key":"20_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","journal-title":"Sci. Data"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, Z., Feng, L., Zhang, W.: Vim: Out-of-distribution with virtual-logit matching. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00487"},{"issue":"3","key":"20_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (csur) 53(3), 1\u201334 (2020)","journal-title":"ACM Comput. Surv. (csur)"},{"issue":"1","key":"20_CR35","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s44267-023-00005-y","volume":"1","author":"Y Yang","year":"2023","unstructured":"Yang, Y., Cui, Z., Xu, J., Zhong, C., Zheng, W.S., Wang, R.: Continual learning with bayesian model based on a fixed pre-trained feature extractor. Visual Intell. 1(1), 5 (2023)","journal-title":"Visual Intell."},{"key":"20_CR36","unstructured":"Yu, G., Zhu, J., Yao, J., Han, B.: Self-calibrated tuning of vision-language models for out-of-distribution detection. NeurIPS (2025)"},{"key":"20_CR37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3369699","volume-title":"Vision-language models for vision tasks: a survey","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Huang, J., Jin, S., Lu, S.: Vision-language models for vision tasks: a survey. IEEE Trans. Patt. Analy. Mach, Intell (2024)"},{"key":"20_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: Tip-adapter: Training-free adaption of CLIP for few-shot classification. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19833-5_29"},{"key":"20_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110250","volume":"149","author":"F Zheng","year":"2024","unstructured":"Zheng, F., Cao, J., Yu, W., Chen, Z., Xiao, N., Lu, Y.: Exploring low-resource medical image classification with weakly supervised prompt learning. Patt. Recogn. 149, 110250 (2024)","journal-title":"Patt. Recogn."},{"key":"20_CR40","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01631"},{"issue":"9","key":"20_CR41","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"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04971-1_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T06:44:58Z","timestamp":1771742698000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04971-1_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049704","9783032049711"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04971-1_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Authors have no competing interests in the paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}