{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T16:05:39Z","timestamp":1771344339294,"version":"3.50.1"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"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":"publisher","award":["62225207"],"award-info":[{"award-number":["62225207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106245"],"award-info":[{"award-number":["62106245"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476260"],"award-info":[{"award-number":["62476260"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s11263-025-02641-x","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T11:51:23Z","timestamp":1768045883000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Boosting Active Prompt Learning via Discriminative Self-Training Dual-Curriculum Learning"],"prefix":"10.1007","volume":"134","author":[{"given":"Sen","family":"Tao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9940-6366","authenticated-orcid":false,"given":"Jiawei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Yongchao","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Bingyu","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Zheng-Jun","family":"Zha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,10]]},"reference":[{"key":"2641_CR1","unstructured":"Ash, J.T., Zhang, C., Krishnamurthy, A., et\u00a0al. (2020). Deep batch active learning by diverse, uncertain gradient lower bounds. In: 8th International Conference on Learning Representations"},{"key":"2641_CR2","doi-asserted-by":"crossref","unstructured":"Bang, J., Ahn, S. & Lee, J.G.(2024). Active prompt learning in vision language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 27004\u201327014","DOI":"10.1109\/CVPR52733.2024.02550"},{"key":"2641_CR3","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., et\u00a0al. (2009). Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp 41\u201348","DOI":"10.1145\/1553374.1553380"},{"issue":"2","key":"2641_CR4","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1111\/j.2517-6161.1964.tb00553.x","volume":"26","author":"GE Box","year":"1964","unstructured":"Box, G. E., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society Series B: Statistical Methodology, 26(2), 211\u2013243.","journal-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology"},{"key":"2641_CR5","unstructured":"Brown, T., Mann, B., Ryder, N., et\u00a0al. (2020). Language models are few-shot learners. In: Advances in Neural Information Processing Systems, pp 1877\u20131901"},{"key":"2641_CR6","doi-asserted-by":"crossref","unstructured":"Caramalau, R., Bhattarai, B. & Kim, T.K. (2021). Sequential graph convolutional network for active learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9583\u20139592","DOI":"10.1109\/CVPR46437.2021.00946"},{"key":"2641_CR7","unstructured":"Chakraborty, O., Sahoo, A., Panda, R. et\u00a0al. (2024). Xpl: A cross-model framework for semi-supervised prompt learning in vision-language models. Transactions on Machine Learning Research"},{"key":"2641_CR8","doi-asserted-by":"crossref","unstructured":"Chen, X. & Gupta, A. (2015). Webly supervised learning of convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1431\u20131439","DOI":"10.1109\/ICCV.2015.168"},{"key":"2641_CR9","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I. et\u00a0al. (2014). Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3606\u20133613","DOI":"10.1109\/CVPR.2014.461"},{"key":"2641_CR10","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, et\u00a0al (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2641_CR11","doi-asserted-by":"crossref","unstructured":"Fei-Fei, L., Fergus, R. & Perona, P. (2004). 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, IEEE, pp 178\u2013178","DOI":"10.1109\/CVPR.2004.383"},{"key":"2641_CR12","unstructured":"Gal, Y., Islam, R. & Ghahramani, Z. (2017). Deep bayesian active learning with image data. In: International Conference on Machine Learning, PMLR, pp 1183\u20131192"},{"issue":"7","key":"2641_CR13","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/TIP.2008.924286","volume":"17","author":"PH Gosselin","year":"2008","unstructured":"Gosselin, P. H., & Cord, M. (2008). Active learning methods for interactive image retrieval. IEEE Transactions on Image Processing, 17(7), 1200\u20131211.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2641_CR14","unstructured":"Graves, A., Bellemare, M.G., Menick, J., et\u00a0al. (2017). Automated curriculum learning for neural networks. In: International Conference on Machine Learning, Pmlr, pp 1311\u20131320"},{"key":"2641_CR15","unstructured":"Hacohen, G. & Weinshall, D. (2019). On the power of curriculum learning in training deep networks. In: International Conference on Machine Learning, PMLR, pp 2535\u20132544"},{"key":"2641_CR16","unstructured":"Hacohen, G., Dekel, A., Weinshall, D. (2022). Active learning on a budget: Opposite strategies suit high and low budgets. In: International Conference on Machine Learning, vol 162. PMLR, pp 8175\u20138195"},{"key":"2641_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. et\u00a0al. (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7","key":"2641_CR18","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., et al. (2019). Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2217\u20132226.","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"2641_CR19","doi-asserted-by":"crossref","unstructured":"Holub, A., Perona, P., Burl, M.C. (2008). Entropy-based active learning for object recognition. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, pp 1\u20138","DOI":"10.1109\/CVPRW.2008.4563068"},{"key":"2641_CR20","doi-asserted-by":"crossref","unstructured":"Hu, B., Zha, Z.J., Liu, J., et\u00a0al. (2021). Cluster and scatter: A multi-grained active semi-supervised learning framework for scalable person re-identification. In: Proceedings of the 29th ACM International Conference on Multimedia, pp 2605\u20132614","DOI":"10.1145\/3474085.3475435"},{"key":"2641_CR21","unstructured":"Huang, T., Chu, J., & Wei, F. (2022). Unsupervised prompt learning for vision-language models. arXiv:2204.03649"},{"key":"2641_CR22","doi-asserted-by":"crossref","unstructured":"Jia, M., Tang, L., Chen, B.C., et\u00a0al. (2022). Visual prompt tuning. In: European Conference on Computer Vision, Springer, pp 709\u2013727","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"2641_CR23","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1162\/tacl_a_00324","volume":"8","author":"Z Jiang","year":"2020","unstructured":"Jiang, Z., Xu, F. F., Araki, J., et al. (2020). How can we know what language models know? Transactions of the Association for Computational Linguistics, 8, 423\u2013438.","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"2641_CR24","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s11263-009-0268-3","volume":"88","author":"A Kapoor","year":"2010","unstructured":"Kapoor, A., Grauman, K., Urtasun, R., et al. (2010). Gaussian processes for object categorization. International Journal of Computer Vision, 88, 169\u2013188.","journal-title":"International Journal of Computer Vision"},{"key":"2641_CR25","doi-asserted-by":"crossref","unstructured":"Karim, N., Mithun, N.C., Rajvanshi, A., et\u00a0al. (2023). C-sfda: A curriculum learning aided self-training framework for efficient source free domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 24120\u201324131","DOI":"10.1109\/CVPR52729.2023.02310"},{"key":"2641_CR26","doi-asserted-by":"crossref","unstructured":"Khattak, M.U., Rasheed, H., Maaz, M., et\u00a0al. (2023). Maple: Multi-modal prompt learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 19113\u201319122","DOI":"10.1109\/CVPR52729.2023.01832"},{"key":"2641_CR27","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P., Teterwak, P., Wang, C., et al. (2020). Supervised contrastive learning. Advances in Neural Information Processing Systems, 33, 18661\u201318673.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2641_CR28","unstructured":"Kirsch, A., Van\u00a0Amersfoort, J. & Gal, Y. (2019). Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Advances in Neural Information Processing Systems 32"},{"key":"2641_CR29","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., et\u00a0al. (2013). 3d object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp 554\u2013561","DOI":"10.1109\/ICCVW.2013.77"},{"issue":"260","key":"2641_CR30","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","volume":"47","author":"WH Kruskal","year":"1952","unstructured":"Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American statistical Association, 47(260), 583\u2013621.","journal-title":"Journal of the American statistical Association"},{"key":"2641_CR31","doi-asserted-by":"crossref","unstructured":"Lewis, D.D. & Catlett, J. (1994). Heterogeneous uncertainty sampling for supervised learning. In: Machine Learning Proceedings 1994. Elsevier, p 148\u2013156","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"issue":"2","key":"2641_CR32","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s11263-015-0834-9","volume":"116","author":"C Long","year":"2016","unstructured":"Long, C., Hua, G., & Kapoor, A. (2016). A joint gaussian process model for active visual recognition with expertise estimation in crowdsourcing. International Journal of Computer Vision, 116(2), 136\u2013160.","journal-title":"International Journal of Computer Vision"},{"key":"2641_CR33","doi-asserted-by":"crossref","unstructured":"Ma, J. (2024). Improved self-training for test-time adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 23701\u201323710","DOI":"10.1109\/CVPR52733.2024.02237"},{"key":"2641_CR34","doi-asserted-by":"crossref","unstructured":"Mahapatra, D., Bozorgtabar, B., Thiran, J.P., et\u00a0al. (2018). Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 580\u2013588","DOI":"10.1007\/978-3-030-00934-2_65"},{"key":"2641_CR35","unstructured":"Maji, S., Rahtu, E., Kannala, J., et\u00a0al. (2013). Fine-grained visual classification of aircraft. arXiv:1306.5151"},{"key":"2641_CR36","doi-asserted-by":"crossref","unstructured":"Mayer, C. & Timofte, R. (2020). Adversarial sampling for active learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 3071\u20133079","DOI":"10.1109\/WACV45572.2020.9093556"},{"key":"2641_CR37","doi-asserted-by":"crossref","unstructured":"Mei, K., Zhu, C., Zou, J., et\u00a0al. (2020). Instance adaptive self-training for unsupervised domain adaptation. In: European conference on computer vision, Springer, pp 415\u2013430","DOI":"10.1007\/978-3-030-58574-7_25"},{"key":"2641_CR38","doi-asserted-by":"crossref","unstructured":"Narr, A., Triebel, R. & Cremers, D. (2016). Stream-based active learning for efficient and adaptive classification of 3d objects. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 227\u2013233","DOI":"10.1109\/ICRA.2016.7487138"},{"key":"2641_CR39","doi-asserted-by":"crossref","unstructured":"Nilsback, M. E., & Zisserman, A. (2008). Automated flower classification over a large number of classes. 2008 Sixth Indian Conference on Computer Vision (pp. 722\u2013729). Graphics & Image Processing: IEEE.","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"2641_CR40","doi-asserted-by":"crossref","unstructured":"Ning, S., Qiu, L., Liu, Y., et\u00a0al. (2023). Hoiclip: Efficient knowledge transfer for hoi detection with vision-language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 23507\u201323517","DOI":"10.1109\/CVPR52729.2023.02251"},{"key":"2641_CR41","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., Zisserman, A., et\u00a0al. (2012). Cats and dogs. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 3498\u20133505","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"2641_CR42","doi-asserted-by":"crossref","unstructured":"Parvaneh, A., Abbasnejad, E., Teney, D., et\u00a0al. (2022). Active learning by feature mixing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12237\u201312246","DOI":"10.1109\/CVPR52688.2022.01192"},{"key":"2641_CR43","unstructured":"Peng, G., Shijie, G., Renrui, Z., et\u00a0al. (2021). Better vision-language models with feature adapters. arXiv:2110.04544 3"},{"key":"2641_CR44","doi-asserted-by":"crossref","unstructured":"Qiu, S., Zhu, C. & Zhou, W. (2021). Meta self-learning for multi-source domain adaptation: a benchmark. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1592\u20131601","DOI":"10.1109\/ICCVW54120.2021.00184"},{"key":"2641_CR45","unstructured":"Radford, A., Kim, J.W., Hallacy, C., et\u00a0al. (2021). Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, PMLR, pp 8748\u20138763"},{"key":"2641_CR46","doi-asserted-by":"crossref","unstructured":"Rakesh, V. & Jain, S. (2021). Efficacy of bayesian neural networks in active learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2601\u20132609","DOI":"10.1109\/CVPRW53098.2021.00294"},{"key":"2641_CR47","unstructured":"Saxena, S., Tuzel, O. & DeCoste, D. (2019) .Data parameters: A new family of parameters for learning a differentiable curriculum. Advances in Neural Information Processing Systems 32"},{"key":"2641_CR48","unstructured":"Sener, O., & Savarese, S. (2017). Active learning for convolutional neural networks: A core-set approach. arXiv:1708.00489"},{"key":"2641_CR49","first-page":"14274","volume":"35","author":"M Shu","year":"2022","unstructured":"Shu, M., Nie, W., Huang, D. A., et al. (2022). Test-time prompt tuning for zero-shot generalization in vision-language models. Advances in Neural Information Processing Systems, 35, 14274\u201314289.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2641_CR50","unstructured":"Shui, C., Zhou, F., Gagn\u00e9, C., et\u00a0al. (2020). Deep active learning: Unified and principled method for query and training. In: International Conference on Artificial Intelligence and Statistics, PMLR, pp 1308\u20131318"},{"issue":"2","key":"2641_CR51","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.jml.2010.11.002","volume":"64","author":"JG Tullis","year":"2011","unstructured":"Tullis, J. G., & Benjamin, A. S. (2011). On the effectiveness of self-paced learning. Journal of Memory and Language, 64(2), 109\u2013118.","journal-title":"Journal of Memory and Language"},{"key":"2641_CR52","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s11263-010-0372-4","volume":"91","author":"S Vijayanarasimhan","year":"2011","unstructured":"Vijayanarasimhan, S., & Grauman, K. (2011). Cost-sensitive active visual category learning. International Journal of Computer Vision, 91, 24\u201344.","journal-title":"International Journal of Computer Vision"},{"key":"2641_CR53","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s11263-014-0721-9","volume":"108","author":"S Vijayanarasimhan","year":"2014","unstructured":"Vijayanarasimhan, S., & Grauman, K. (2014). Large-scale live active learning: Training object detectors with crawled data and crowds. International Journal of Computer Vision, 108, 97\u2013114.","journal-title":"International Journal of Computer Vision"},{"key":"2641_CR54","doi-asserted-by":"crossref","unstructured":"Wang. A., Islam, M., Xu, M., et\u00a0al. (2023). Curriculum-based augmented fourier domain adaptation for robust medical image segmentation. IEEE Transactions on Automation Science and Engineering","DOI":"10.1109\/TASE.2023.3295600"},{"issue":"1","key":"2641_CR55","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TIP.2018.2867913","volume":"28","author":"G Wang","year":"2019","unstructured":"Wang, G., Hwang, J. N., Rose, C., et al. (2019). Uncertainty-based active learning via sparse modeling for image classification. IEEE Transactions on Image Processing, 28(1), 316\u2013329.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2641_CR56","doi-asserted-by":"crossref","unstructured":"Wang, Q., Fink, O., Van\u00a0Gool, L., et\u00a0al. (2022). Continual test-time domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7201\u20137211","DOI":"10.1109\/CVPR52688.2022.00706"},{"issue":"9","key":"2641_CR57","first-page":"4555","volume":"44","author":"X Wang","year":"2021","unstructured":"Wang, X., Chen, Y., & Zhu, W. (2021). A survey on curriculum learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9), 4555\u20134576.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2641_CR58","doi-asserted-by":"crossref","unstructured":"Wan, Y., Hong, J., Cheraghian, A., et\u00a0al. (2024). Continual test-time domain adaptation via dynamic sample selection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 1701\u20131710","DOI":"10.1109\/WACV57701.2024.00172"},{"key":"2641_CR59","unstructured":"Weinshall D, Cohen G, Amir D (2018) Curriculum learning by transfer learning: Theory and experiments with deep networks. In: International Conference on Machine Learning, PMLR, pp 5238\u20135246"},{"key":"2641_CR60","doi-asserted-by":"crossref","unstructured":"Xiao, J., Hays, J., Ehinger, K.A,. et\u00a0al. (2010). Sun database: Large-scale scene recognition from abbey to zoo. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp 3485\u20133492","DOI":"10.1109\/CVPR.2010.5539970"},{"key":"2641_CR61","doi-asserted-by":"crossref","unstructured":"Xie, Y., Lu, H., Yan, J., et\u00a0al. (2023). Active finetuning: Exploiting annotation budget in the pretraining-finetuning paradigm. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 23715\u201323724","DOI":"10.1109\/CVPR52729.2023.02271"},{"key":"2641_CR62","doi-asserted-by":"crossref","unstructured":"Xu, B., Zhang, L., Mao, Z., et\u00a0al. (2020). Curriculum learning for natural language understanding. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 6095\u20136104","DOI":"10.18653\/v1\/2020.acl-main.542"},{"key":"2641_CR63","doi-asserted-by":"crossref","unstructured":"Yan, S., Dong, N., Zhang, L., et\u00a0al. (2023). Clip-driven fine-grained text-image person re-identification. IEEE Transactions on Image Processing","DOI":"10.1109\/TIP.2023.3327924"},{"key":"2641_CR64","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s11263-014-0781-x","volume":"113","author":"Y Yang","year":"2015","unstructured":"Yang, Y., Ma, Z., Nie, F., et al. (2015). Multi-class active learning by uncertainty sampling with diversity maximization. International Journal of Computer Vision, 113, 113\u2013127.","journal-title":"International Journal of Computer Vision"},{"key":"2641_CR65","doi-asserted-by":"crossref","unstructured":"Yun, S., Park, S.H., Seo, P.H., et\u00a0al.(2023). Ifseg: Image-free semantic segmentation via vision-language model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2967\u20132977","DOI":"10.1109\/CVPR52729.2023.00290"},{"key":"2641_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, X., Shapiro, P., Kumar, G., et\u00a0al. (2019). Curriculum learning for domain adaptation in neural machine translation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Minneapolis, Minnesota, pp 1903\u20131915","DOI":"10.18653\/v1\/N19-1189"},{"key":"2641_CR67","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., et\u00a0al. (2022a). Conditional prompt learning for vision-language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 16816\u201316825","DOI":"10.1109\/CVPR52688.2022.01631"},{"issue":"9","key":"2641_CR68","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., et al. (2022). Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9), 2337\u20132348.","journal-title":"International Journal of Computer Vision"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02641-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-025-02641-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-025-02641-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T15:19:20Z","timestamp":1771341560000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-025-02641-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,10]]},"references-count":68,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["2641"],"URL":"https:\/\/doi.org\/10.1007\/s11263-025-02641-x","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,10]]},"assertion":[{"value":"29 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"52"}}