{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:49:17Z","timestamp":1785919757001,"version":"3.56.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032314376","type":"print"},{"value":"9783032314383","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-31438-3_12","type":"book-chapter","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:15:54Z","timestamp":1785917754000},"page":"170-184","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Neuro-Symbolic Adapter for\u00a0Efficient Fine-Grained Visual Recognition"],"prefix":"10.1007","author":[{"given":"Guanyu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuhong","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"12_CR1","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":"12_CR2","doi-asserted-by":"crossref","unstructured":"Karmanov, A., Guan, D., Lu, S., El\u00a0Saddik, A., Xing, E.: Efficient test-time adaptation of vision-language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14162\u201314171 (2024)","DOI":"10.1109\/CVPR52733.2024.01343"},{"key":"12_CR3","doi-asserted-by":"publisher","unstructured":"Zhang, R., et al.: Tip-adapter: training-free adaption of clip for few-shot classification. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022, pp. 493\u2013510. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19833-5_29","DOI":"10.1007\/978-3-031-19833-5_29"},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Ye, Z., Hua, Y., Zhang, W., Song, X., Feng, Z., Wu, X.J.: A novel loss for contrastive deep supervision. In: 2024 27th International Conference on Pattern Recognition, pp. 1\u201316. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-78389-0_1","DOI":"10.1007\/978-3-031-78389-0_1"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Zanella, M., Fuchs, C., Ayed, I.B., De\u00a0Vleeschouwer, C.: Vocabulary-free few-shot learning for vision-language models. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 149\u2013158 (2025)","DOI":"10.1109\/CVPRW67362.2025.00020"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Jonathan St, B.T., Evans.: In two minds: dual-process accounts of reasoning. Trends Cogn. Sci. 7(10), 454\u2013459 (2003)","DOI":"10.1016\/j.tics.2003.08.012"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Ding, J., Liu, T., Chen, Q., Yuan, Z., Shang, Y.: Visual saliency oriented vehicle scale estimation. In: 2020 25th International Conference on Pattern Recognition, pp. 1867\u20131873. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412618"},{"issue":"3","key":"12_CR8","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379\u2013423 (1948)","journal-title":"Bell Syst. Tech. J."},{"issue":"11","key":"12_CR9","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"12_CR10","unstructured":"Yuan, L., et\u00a0al.: Florence: a new foundation model for computer vision. arXiv preprint arXiv:2111.11432 (2021)"},{"key":"12_CR11","doi-asserted-by":"crossref","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)","DOI":"10.1007\/s11263-022-01653-1"},{"key":"12_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":"12_CR13","doi-asserted-by":"crossref","unstructured":"Fuchs, C., Zanella, M., De\u00a0Vleeschouwer, C.: Online gaussian test-time adaptation of vision-language models. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 128\u2013137 (2025)","DOI":"10.1109\/CVPRW67362.2025.00018"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Fan, X., et al.: Test-time retrieval-augmented adaptation for vision-language models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8810\u20138819 (2025)","DOI":"10.1109\/ICCV51701.2025.00824"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"d\u2019Avila Garcez, A., Lamb, L.C.: Neurosymbolic AI: the 3 rd wave. Artif. Intell. Rev. 56(11), 12387\u201312406 (2023)","DOI":"10.1007\/s10462-023-10448-w"},{"key":"12_CR16","first-page":"1","volume":"31","author":"R Manhaeve","year":"2018","unstructured":"Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., De Raedt, L.: Deepproblog: neural probabilistic logic programming. Adv. Neural. Inf. Process. Syst. 31, 1\u201311 (2018)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR17","unstructured":"Menon, S., Vondrick, C.: Visual classification via description from large language models. arXiv preprint arXiv:2210.07183 (2022)"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Teerapittayanon, S., McDanel, B., Kung, H.T.: Branchynet: fast inference via early exiting from deep neural networks. In: 2016 23rd International Conference on Pattern Recognition, pp. 2464\u20132469. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"12_CR19","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.V.: Cats and dogs. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3498\u20133505. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pp. 722\u2013729. IEEE (2008)","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"12_CR22","unstructured":"Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: 2004 Conference on Computer Vision and Pattern Recognition Workshop, pp. 178\u2013178. IEEE (2004)"},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554\u2013561 (2013)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3606\u20133613 (2014)","DOI":"10.1109\/CVPR.2014.461"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3485\u20133492. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539970"},{"issue":"7","key":"12_CR26","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","volume":"12","author":"P Helber","year":"2019","unstructured":"Helber, P., Bischke, B., Dengel, A., Borth, D.: Eurosat: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE J. Selected Topics Appl. Earth Observat. Remote Sens. 12(7), 2217\u20132226 (2019)","journal-title":"IEEE J. Selected Topics Appl. Earth Observat. Remote Sens."},{"key":"12_CR27","unstructured":"Maji, S., Rahtu, E., Kannala, J., Blaschko, M., Vedaldi, A.: Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151 (2013)"},{"key":"12_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/978-3-319-10599-4_29","volume-title":"Computer Vision \u2013 ECCV 2014","author":"L Bossard","year":"2014","unstructured":"Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 \u2013 mining discriminative components with random forests. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 446\u2013461. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_29"},{"key":"12_CR29","unstructured":"Soomro, K., Amir Roshan Zamir, M., Shah: Ucf101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)"},{"key":"12_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: Prompt, generate, then cache: cascade of foundation models makes strong few-shot learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15211\u201315222 (2023)","DOI":"10.1109\/CVPR52729.2023.01460"},{"key":"12_CR31","doi-asserted-by":"crossref","unstructured":"Udandarao, V., Gupta, A., Albanie, S.: Sus-x: training-free name-only transfer of vision-language models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2725\u20132736 (2023)","DOI":"10.1109\/ICCV51070.2023.00257"},{"key":"12_CR32","doi-asserted-by":"publisher","first-page":"14274","DOI":"10.52202\/068431-1038","volume":"35","author":"M Shu","year":"2022","unstructured":"Shu, M., et al.: Test-time prompt tuning for zero-shot generalization in vision-language models. Adv. Neural. Inf. Process. Syst. 35, 14274\u201314289 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR33","unstructured":"Liu, A., et al.: Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437 (2024)"},{"key":"12_CR34","doi-asserted-by":"publisher","first-page":"1693988","DOI":"10.3389\/frobt.2025.1693988","volume":"12","author":"J Park","year":"2025","unstructured":"Park, J., Kim, P., Ko, D.: Real-time open-vocabulary perception for mobile robots on edge devices: a systematic analysis of the accuracy-latency trade-off. Front. Rob. AI 12, 1693988 (2025)","journal-title":"Front. Rob. AI"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-31438-3_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:16:10Z","timestamp":1785917770000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-31438-3_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,6]]},"ISBN":["9783032314376","9783032314383"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-31438-3_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,6]]},"assertion":[{"value":"6 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lyon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2026","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":"icpr2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}