{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:19:47Z","timestamp":1792783187152,"version":"4.5.0"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the National Key R&D Program of China","award":["2021ZD0111902"],"award-info":[{"award-number":["2021ZD0111902"]}]},{"name":"the National Natural Science Foundation of China","award":["62206007"],"award-info":[{"award-number":["62206007"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"name":"the National Natural Science Foundation of China","award":["U21B2038"],"award-info":[{"award-number":["U21B2038"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Generalized zero-shot learning (GZSL) addresses the challenging task of recognizing both seen and unseen classes by leveraging shared semantic knowledge. A core challenge in this domain is achieving robust visual-semantic alignment to transfer knowledge from seen classes to novel classes. Current state-of-the-art methods typically fine-tune large-scale visual backbones on scarce training data. However, this approach frequently leads to severe overfitting to seen classes, which significantly degrades performance on novel categories.  To mitigate this issue, we propose the Dual Adaptive Visual-Semantic Prompt Collaboration Network (VSPCN+), a novel framework that utilizes prompt-tuning for effective feature adaptation. Our method introduces a dual-prompt mechanism comprising both visual and semantic prompts. The semantic prompts guide the visual encoder to learn visual features that are more semantically consistent with class attributes, while the visual prompts steer the semantic encoder to generate semantic representations that are more visually grounded. This collaborative process enhances the overall visual-semantic consistency. A key innovation of our work is the dynamic generation of instance-adaptive prompts, which contrasts with existing prompt-learning methods that rely on static, global prompts. By tailoring prompts to individual instances, our approach enhances the model\u2019s robustness and generalization capabilities across diverse visual inputs. This collaborative adaptation, guided by our dual-prompt mechanism, allows the visual and semantic encoders to produce consistent representations for effective visual-semantic alignment. Extensive experiments on standard GZSL benchmarks demonstrate that our proposed VSPCN+ performs favorably against several state-of-the-art methods.<\/jats:p>","DOI":"10.1007\/s11263-026-02998-7","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T05:42:39Z","timestamp":1788500559000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual Adaptive Visual-Semantic Prompt Collaboration for Generalized Zero-Shot Learning"],"prefix":"10.1007","volume":"134","author":[{"given":"Huajie","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengxian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4312-5682","authenticated-orcid":false,"given":"Yuankai","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongli","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amin","family":"Beheshti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Hsuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"2998_CR1","unstructured":"Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning arXiv:2108.00045 arXiv preprint."},{"key":"2998_CR2","doi-asserted-by":"crossref","unstructured":"Alamri, Faisal & Dutta, Anjan (2021) Implicit and explicit attention for zero-shot learning. In: GCPR, pp 467\u2013483.","DOI":"10.1007\/978-3-030-92659-5_30"},{"key":"2998_CR3","first-page":"213","volume":"12346","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. ECCV, 12346, 213\u2013229.","journal-title":"ECCV"},{"key":"2998_CR4","unstructured":"Chen, G., Yao, W., Song, X., Li, X., Rao, Y., & Zhang, K. (2023a). PLOT: prompt learning with optimal transport for vision-language models. In: ICLR."},{"key":"2998_CR5","doi-asserted-by":"crossref","unstructured":"Chen, S., Wang, W., Xia, B., Peng, Q., You, X., Zheng, F., Shao, L. (2021a). Free: Feature refinement for generalized zero-shot learning. In: ICCV, pp 122\u2013131.","DOI":"10.1109\/ICCV48922.2021.00019"},{"key":"2998_CR6","unstructured":"Chen, S., Xie, G., Liu, Y., Peng, Q., Sun, B., Li, H., You, X., & Shao, L. (2021b). Hsva: Hierarchical semantic-visual adaptation for zero-shot learning. In: NeurIPS, pp 16622\u201316634."},{"key":"2998_CR7","doi-asserted-by":"crossref","unstructured":"Chen, S., Hong, Z., Liu, Y., Xie, G. S., Sun, B., Li, H., Peng, Q., Lu, K., & You, X. (2022a). Transzero: Attribute-guided transformer for zero-shot learning. In: AAAI, pp 330\u2013338.","DOI":"10.1609\/aaai.v36i1.19909"},{"key":"2998_CR8","doi-asserted-by":"crossref","unstructured":"Chen, S., Hong, Z., Xie, G. S., Yang, W., Peng, Q., Wang, K., Zhao, J., & You, X. (2022b). Msdn: Mutually semantic distillation network for zero-shot learning. In: CVPR, pp 7612\u20137621.","DOI":"10.1109\/CVPR52688.2022.00746"},{"key":"2998_CR9","first-page":"12844","volume":"45","author":"S Chen","year":"2023","unstructured":"Chen, S., Hong, Z., Hou, W., Xie, G. S., Song, Y., Zhao, J., You, X., Yan, S., & Shao, L. (2023). Transzero++: Cross attribute-guided transformer for zero-shot learning. IEEE Trans Pattern Anal Mach Intell, 45, 12844\u201312861.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2998_CR10","first-page":"4611","volume":"202","author":"S Chen","year":"2023","unstructured":"Chen, S., Hou, W., Hong, Z., Ding, X., Song, Y., You, X., Liu, T., & Zhang, K. (2023). Evolving semantic prototype improves generative zero-shot learning. ICML, 202, 4611\u20134622.","journal-title":"ICML"},{"key":"2998_CR11","doi-asserted-by":"crossref","unstructured":"Chen, S., Hou, W., Khan, S., & Khan, F. S. (2024a). Progressive semantic-guided vision transformer for zero-shot learning. In: CVPR, pp 23964\u201323974.","DOI":"10.1109\/CVPR52733.2024.02262"},{"key":"2998_CR12","doi-asserted-by":"publisher","first-page":"4504","DOI":"10.1007\/s11263-025-02394-7","volume":"133","author":"S Chen","year":"2025","unstructured":"Chen, S., Hong, Z., You, X., & Shao, L. (2025). Semantics-conditioned generative zero-shot learning via feature refinement. International Journal of Computer Vision, 133, 4504\u20134521.","journal-title":"International Journal of Computer Vision"},{"key":"2998_CR13","doi-asserted-by":"crossref","unstructured":"Chen, W., Si, C., Zhang, Z., Wang, L., Wang, Z., & Tan, T. (2023d). Semantic prompt for few-shot image recognition. In: CVPR, pp 23581\u201323591.","DOI":"10.1109\/CVPR52729.2023.02258"},{"key":"2998_CR14","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1109\/TPAMI.2023.3325533","volume":"46","author":"X Chen","year":"2024","unstructured":"Chen, X., Deng, X., Lan, Y., Long, Y., Weng, J., Liu, Z., & Tian, Q. (2024). Explanatory object part aggregation for zero-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 851\u2013868.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2998_CR15","doi-asserted-by":"crossref","unstructured":"Chen, Z., Huang, Y., Chen, J., Geng, Y., Zhang, W., Fang, Y., Pan, J. Z., & Chen, H. (2023e). Duet: Cross-modal semantic grounding for contrastive zero-shot learning. In: AAAI, pp 405\u2013413.","DOI":"10.1609\/aaai.v37i1.25114"},{"key":"2998_CR16","doi-asserted-by":"crossref","unstructured":"Chen, Z., Huang, Y., Chen, J., Geng, Y., Zhang, W., Fang, Y., Z\u00a0Pan, J., & Chen, H. (2023f). Duet: Cross-modal semantic grounding for contrastive zero-shot learning. AAAI pp 405\u2013413.","DOI":"10.1609\/aaai.v37i1.25114"},{"key":"2998_CR17","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhao, Z., Guo, J., Li, J., & Huang, Z. (2025b). SVIP: semantically contextualized visual patches for zero-shot learning. In: ICCV.","DOI":"10.1109\/ICCV51701.2025.00320"},{"key":"2998_CR18","doi-asserted-by":"crossref","unstructured":"Christensen, A., Mancini, M., Koepke, A., Winther, O., & Akata, Z. (2023). Image-free classifier injection for zero-shot classification. ICCV (pp. 19072\u201319081).","DOI":"10.1109\/ICCV51070.2023.01748"},{"key":"2998_CR19","doi-asserted-by":"crossref","unstructured":"Clark, K., & Jaini, P. (2024). Text-to-image diffusion models are zero shot classifiers. NeruIPS.","DOI":"10.52202\/075280-2571"},{"key":"2998_CR20","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. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. ICLR."},{"key":"2998_CR21","first-page":"5641","volume":"34","author":"M Gao","year":"2024","unstructured":"Gao, M., & Dong, Q. (2024). Adaptive conditional denoising diffusion model with hybrid affinity regularizer for generalized zero-shot learning. TCSVT, 34, 5641\u20135652.","journal-title":"TCSVT"},{"key":"2998_CR22","doi-asserted-by":"publisher","first-page":"14611","DOI":"10.1109\/TPAMI.2023.3295772","volume":"45","author":"A Gupta","year":"2023","unstructured":"Gupta, A., Narayan, S., Khan, S., Khan, F. S., Shao, L., & Van De Weijer, J. (2023). Generative multi-label zero-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 14611\u201314624.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2998_CR23","doi-asserted-by":"crossref","unstructured":"Han, Z., Fu, Z., Chen, S., & Yang, J. (2021). Contrastive embedding for generalized zero-shot learning (pp. 2371\u20132381). CVPR.","DOI":"10.1109\/CVPR46437.2021.00240"},{"key":"2998_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition (pp. 770\u2013778). CVPR.","DOI":"10.1109\/CVPR.2016.90"},{"key":"2998_CR25","doi-asserted-by":"crossref","unstructured":"Hou, W., Chen, S., Chen, S., Hong, Z., Wang, Y., Feng, X., Khan, S., Khan, F. S., & You, X. (2024). Visual-augmented dynamic semantic prototype for generative zero-shot learning. In: CVPR, pp 23627\u201323637.","DOI":"10.1109\/CVPR52733.2024.02230"},{"key":"2998_CR26","doi-asserted-by":"crossref","unstructured":"Hou, W., Fu, D., Li, K., Chen, S., Fan, H., & Yang, Y. (2025). Zeromamba: Exploring visual state space model for zero-shot learning (pp. 3527\u20133535). AAAI.","DOI":"10.1609\/aaai.v39i4.32366"},{"key":"2998_CR27","doi-asserted-by":"publisher","first-page":"3180","DOI":"10.1109\/TCSVT.2023.3313727","volume":"34","author":"Y Hu","year":"2024","unstructured":"Hu, Y., Feng, L., Jiang, H., Liu, M., & Yin, B. (2024). Domain-aware prototype network for generalized zero-shot learning. IEEE Transactions on Circuits and Systems for Video Technology, 34, 3180\u20133191.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"5","key":"2998_CR28","doi-asserted-by":"publisher","first-page":"3180","DOI":"10.1109\/TCSVT.2023.3313727","volume":"34","author":"Y Hu","year":"2024","unstructured":"Hu, Y., Feng, L., Jiang, H., Liu, M., & Yin, B. (2024). Domain-aware prototype network for generalized zero-shot learning. IEEE Transactions on Circuits and Systems for Video Technology, 34(5), 3180\u20133191.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"2998_CR29","doi-asserted-by":"crossref","unstructured":"Huynh, D., & Elhamifar, E. (2020). Fine-grained generalized zero-shot learning via dense attribute-based attention (pp. 4482\u20134492). CVPR.","DOI":"10.1109\/CVPR42600.2020.00454"},{"key":"2998_CR30","doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, R., Shan, S., Yang, Y., & Chen, X. (2017). Learning discriminative latent attributes for zero-shot classification. ICCV (pp. 4233\u20134242).","DOI":"10.1109\/ICCV.2017.453"},{"key":"2998_CR31","doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, R., Shan, S., & Chen, X. (2018). Learning class prototypes via structure alignment for zero-shot recognition. ECCV,11214, 121\u2013138.","DOI":"10.1007\/978-3-030-01249-6_8"},{"key":"2998_CR32","doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, R., Shan, S., & Chen, X. (2018b). Learning class prototypes via structure alignment for zero-shot recognition. In: ECCV, pp 121\u2013138.","DOI":"10.1007\/978-3-030-01249-6_8"},{"key":"2998_CR33","doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, R., Shan, S., & Chen, X. (2019a). Transferable contrastive network for generalized zero-shot learning. In: ICCV, pp 9764\u20139773.","DOI":"10.1109\/ICCV.2019.00986"},{"key":"2998_CR34","doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, R., Shan, S., & Chen, X. (2019b). Transferable contrastive network for generalized zero-shot learning. In: ICCV, pp 9764\u20139773.","DOI":"10.1109\/ICCV.2019.00986"},{"issue":"2","key":"2998_CR35","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1109\/TCSVT.2024.3474910","volume":"35","author":"H Jiang","year":"2024","unstructured":"Jiang, H., Li, Z., Hu, Y., Yin, B., Yang, J., Hengel, A. V. D., Yang, M. H., & Qi, Y. (2024). Dual prototype contrastive network for generalized zero-shot learning. IEEE Transactions on Circuits and Systems for Video Technology, 35(2), 1111\u20131122.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"2998_CR36","doi-asserted-by":"crossref","unstructured":"Jiang, H., Li, Z., Yu, X., Hu, Y., Yin, B., Yang, J., & Qi, Y. (2025). Visual and semantic prompt collaboration for generalized zero-shot learning. CVPR.","DOI":"10.1109\/CVPR52734.2025.01888"},{"key":"2998_CR37","doi-asserted-by":"crossref","unstructured":"Khattak, M. U., Rasheed, H. A., Maaz, M., Khan, S. H., & Khan, F. S. (2023). Maple: Multi-modal prompt learning. In: CVPR, pp 19113\u201319122.","DOI":"10.1109\/CVPR52729.2023.01832"},{"key":"2998_CR38","doi-asserted-by":"crossref","unstructured":"Kong, X., Gao, Z., Li, X., Hong, M., Liu, J., Wang, C., Xie, Y., & Qu, Y. (2022). En-compactness: Self-distillation embedding & contrastive generation for generalized zero-shot learning. In: CVPR, pp 9296\u20139305.","DOI":"10.1109\/CVPR52688.2022.00909"},{"key":"2998_CR39","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22, 79\u201386.","journal-title":"The Annals of Mathematical Statistics"},{"key":"2998_CR40","doi-asserted-by":"crossref","unstructured":"Lampert, C. H., Nickisch, H., & Harmeling, S. (2009). Learning to detect unseen object classes by between-class attribute transfer (pp. 951\u2013958). CVPR.","DOI":"10.1109\/CVPR.2009.5206594"},{"key":"2998_CR41","doi-asserted-by":"crossref","unstructured":"Lei\u00a0Ba, J., Swersky, K., Fidler, S., & salakhutdinov, R. (2015). Predicting deep zero-shot convolutional neural networks using textual descriptions. In: ICCV, pp 4247\u20134255.","DOI":"10.1109\/ICCV.2015.483"},{"key":"2998_CR42","doi-asserted-by":"crossref","unstructured":"Li, J., Jing, M., Lu, K., Ding, Z., Zhu, L., & Huang, Z. (2019). Leveraging the invariant side of generative zero-shot learning (pp. 7394\u20137403). CVPR.","DOI":"10.1109\/CVPR.2019.00758"},{"key":"2998_CR43","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, Y., Bian, S., Qu, Y., Xie, Y., Shi, Z., & Fan, J. (2023). Vs-boost: Boosting visual-semantic association for generalized zero-shot learning. IJCAI, 1107\u20131115.","DOI":"10.24963\/ijcai.2023\/123"},{"key":"2998_CR44","doi-asserted-by":"crossref","unstructured":"Liu, M., Li, F., Zhang, C., Wei, Y., Bai, H., & Zhao, Y. (2023). Progressive semantic-visual mutual adaption for generalized zero-shot learning (pp. 15337\u201315346). CVPR.","DOI":"10.1109\/CVPR52729.2023.01472"},{"key":"2998_CR45","doi-asserted-by":"crossref","unstructured":"Liu, M., Bai, H., Li, F., Zhang, C., Wei, Y., Chua, T., & Zhao, Y. (2025). Attend and enrich: Enhanced visual prompt for zero-shot learning (pp. 5504\u20135512). AAAI.","DOI":"10.1609\/aaai.v39i5.32586"},{"key":"2998_CR46","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhou, L., Bai, X., Huang, Y., Gu, L., Zhou, J., & Harada, T. (2021). Goal-oriented gaze estimation for zero-shot learning. In: CVPR, pp 3794\u20133803.","DOI":"10.1109\/CVPR46437.2021.00379"},{"key":"2998_CR47","doi-asserted-by":"crossref","unstructured":"Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., & Hu, H. (2022). Video swin transformer (pp. 3192\u20133201). CVPR.","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"2998_CR48","doi-asserted-by":"crossref","unstructured":"Lu, X., Guo, S., Liu, Z., & Guo, J. (2023). Decomposed soft prompt guided fusion enhancing for compositional zero-shot learning. In: CVPR, pp 23560\u201323569.","DOI":"10.1109\/CVPR52729.2023.02256"},{"key":"2998_CR49","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9, 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"2998_CR50","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1109\/TIP.2025.3531697","volume":"34","author":"M Gao","year":"2025","unstructured":"Gao, M., & Dong, Q. (2025). Self-assembled generative framework for generalized zero-shot learning. IEEE Transactions on Image Processing, 34, 914\u2013924.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2998_CR51","doi-asserted-by":"crossref","unstructured":"Min, S., Yao, H., Xie, H., Wang, C., Zha, Z. J., & Zhang, Y. (2020). Domain-aware visual bias eliminating for generalized zero-shot learning. In: CVPR, pp 12664\u201312673.","DOI":"10.1109\/CVPR42600.2020.01268"},{"key":"2998_CR52","doi-asserted-by":"crossref","unstructured":"Morgado, P., & Vasconcelos, N. (2017). Semantically consistent regularization for zero-shot recognition. CVPR (pp. 2037\u20132046).","DOI":"10.1109\/CVPR.2017.220"},{"key":"2998_CR53","doi-asserted-by":"crossref","unstructured":"Narayan, S., Gupta, A., Khan, F. S., Snoek, C. G., & Shao, L. (2020). Latent embedding feedback and discriminative features for zero-shot classification (pp. 479\u2013495). ECCV.","DOI":"10.1007\/978-3-030-58542-6_29"},{"key":"2998_CR54","unstructured":"Nayak, N. V., Yu, P., & Bach, S. H. (2023). Learning to compose soft prompts for compositional zero-shot learning. ICLR."},{"key":"2998_CR55","unstructured":"Palatucci, M., Pomerleau, D., Hinton, G. E., & Mitchell, T. M. (2009). Zero-shot learning with semantic output codes. NeurIPS (pp. 1410\u20131418)."},{"key":"2998_CR56","doi-asserted-by":"crossref","unstructured":"Patterson, G., & Hays, J. (2012). Sun attribute database: Discovering, annotating, and recognizing scene attributes (pp. 2751\u20132758). CVPR.","DOI":"10.1109\/CVPR.2012.6247998"},{"key":"2998_CR57","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. empirical methods in natural language processing (EMNLP) (pp. 1532\u20131543).","DOI":"10.3115\/v1\/D14-1162"},{"key":"2998_CR58","doi-asserted-by":"crossref","unstructured":"Reed, S., Akata, Z., Lee, H., & Schiele, B. (2016a). Learning deep representations of fine-grained visual descriptions. In: CVPR, pp 49\u201358.","DOI":"10.1109\/CVPR.2016.13"},{"key":"2998_CR59","doi-asserted-by":"crossref","unstructured":"Reed, S. E., Akata, Z., Lee, H., & Schiele, B. (2016b). Learning deep representations of fine-grained visual descriptions. In: CVPR, pp 49\u201358.","DOI":"10.1109\/CVPR.2016.13"},{"key":"2998_CR60","doi-asserted-by":"crossref","unstructured":"Song, J., Shen, C., Yang, Y., Liu, Y., & Song, M. (2018). Transductive unbiased embedding for zero-shot learning. In: CVPR, pp 1024\u20131033.","DOI":"10.1109\/CVPR.2018.00113"},{"key":"2998_CR61","doi-asserted-by":"crossref","unstructured":"Strudel, R., Garcia, R., Laptev, I., & Schmid, C. (2021). Segmenter: Transformer for semantic segmentation. ICCV (pp. 7242\u20137252).","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"2998_CR62","doi-asserted-by":"crossref","unstructured":"Su, H., Li, J., Chen, Z., Zhu, L., & Lu, K. (2022). Distinguishing unseen from seen for generalized zero-shot learning (pp. 7875\u20137884). CVPR.","DOI":"10.1109\/CVPR52688.2022.00773"},{"key":"2998_CR63","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & J\u00e9gou, H. (2021). Training data-efficient image transformers & distillation through attention. ICML,139, 10347\u201310357."},{"key":"2998_CR64","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need (pp. 5998\u20136008). NeurIPS."},{"key":"2998_CR65","doi-asserted-by":"crossref","unstructured":"Verma, V. K., Arora, G., Mishra, A., & Rai, P. (2018). Generalized zero-shot learning via synthesized examples (pp. 4281\u20134289). CVPR.","DOI":"10.1109\/CVPR.2018.00450"},{"key":"2998_CR66","doi-asserted-by":"crossref","unstructured":"Vyas, M. R., Venkateswara, H., & Panchanathan, S. (2020). Leveraging seen and unseen semantic relationships for generative zero-shot learning (pp. 70\u201386). ECCV.","DOI":"10.1007\/978-3-030-58577-8_5"},{"key":"2998_CR67","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., & Belongie, S. (2011). Caltech-ucsd birds-200-2011. California Institute of Technology. CNS-TR-2011-001 Tech. Rep."},{"key":"2998_CR68","unstructured":"Wang, C., Min, S., Chen, X., Sun, X., & Li, H. (2021). Dual progressive prototype network for generalized zero-shot learning (pp. 2936\u20132948). NeurIPS."},{"key":"2998_CR69","doi-asserted-by":"crossref","unstructured":"Wang, X., Ye, Y., & Gupta, A. (2018). Zero-shot recognition via semantic embeddings and knowledge graphs (pp. 6857\u20136866). CVPR.","DOI":"10.1109\/CVPR.2018.00717"},{"key":"2998_CR70","doi-asserted-by":"crossref","unstructured":"Wang, Y., Hong, M., Huangfu, L., & Huang, S. (2024). Data distribution distilled generative model for generalized zero-shot recognition (pp. 5695\u20135703). AAAI.","DOI":"10.1609\/aaai.v38i6.28381"},{"key":"2998_CR71","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, Z., Ebrahimi, S., Sun, R., Zhang, H., Lee, C. Y., Ren, X., Su, G., Perot, V., Dy, J., et\u00a0al. (2022a). Dualprompt: Complementary prompting for rehearsal-free continual learning. In: ECCV, pp 631\u2013648.","DOI":"10.1007\/978-3-031-19809-0_36"},{"key":"2998_CR72","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, Z., Lee, C. Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., & Pfister, T. (2022b). Learning to prompt for continual learning. In: CVPR, pp 139\u2013149.","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"2998_CR73","doi-asserted-by":"crossref","unstructured":"Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., & Schiele, B. (2016). Latent embeddings for zero-shot classification (pp. 69\u201377). CVPR.","DOI":"10.1109\/CVPR.2016.15"},{"key":"2998_CR74","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2019","unstructured":"Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2019). Zero-shot learning\u2013a comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 2251\u20132265.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2998_CR75","doi-asserted-by":"crossref","unstructured":"Xie, G. S., Liu, L., Jin, X., Zhu, F., Zhang, Z., Qin, J., Yao, Y., & Shao, L. (2019a). Attentive region embedding network for zero-shot learning. In: CVPR, pp 9376\u20139385.","DOI":"10.1109\/CVPR.2019.00961"},{"key":"2998_CR76","doi-asserted-by":"crossref","unstructured":"Xie, G. S., Liu, L., Jin, X., Zhu, F., Zhang, Z., Qin, J., Yao, Y., & Shao, L. (2019b). Attentive region embedding network for zero-shot learning. In: CVPR, pp 9384\u20139393.","DOI":"10.1109\/CVPR.2019.00961"},{"key":"2998_CR77","unstructured":"Xu, W., Xian, Y., Wang, J., Schiele, B., & Akata, Z. (2020). Attribute prototype network for zero-shot learning. NeurIPS."},{"key":"2998_CR78","doi-asserted-by":"crossref","unstructured":"Yu, D., Liu, X., & Yang, B. (2024). Zero-shot image classification with logic adapter and rule prompt (pp. 2075\u20132084). ACM.","DOI":"10.1145\/3589334.3645554"},{"key":"2998_CR79","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xiang, T., & Gong, S. (2017a). Learning a deep embedding model for zero-shot learning. In: CVPR, pp 3010\u20133019.","DOI":"10.1109\/CVPR.2017.321"},{"key":"2998_CR80","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xiang, T., & Gong, S. (2017b). Learning a deep embedding model for zero-shot learning. In: CVPR, pp 2021\u20132030.","DOI":"10.1109\/CVPR.2017.321"},{"key":"2998_CR81","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106227","volume":"174","author":"X Zhang","year":"2024","unstructured":"Zhang, X., Liu, Y., Dang, Y., Gao, X., Han, J., & Shao, L. (2024). Adaptive relation-aware network for zero-shot classification. Neural Networks, 174, Article 106227.","journal-title":"Neural Networks"},{"key":"2998_CR82","doi-asserted-by":"crossref","unstructured":"Zhao, P., Wang, Q., & Yin, Y. (2023). M3r: Masked token mixup and cross-modal reconstruction for zero-shot learning (pp. 3161\u20133171). ACM.","DOI":"10.1145\/3581783.3612104"},{"key":"2998_CR83","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., & Zhang, L. (2021). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR, pp 6881\u20136890.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"2998_CR84","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022a). Conditional prompt learning for vision-language models. In: CVPR, pp 16795\u201316804.","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"2998_CR85","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. (2022). Learning to prompt for vision-language models. International Journal of Computer Vision, 130, 2337\u20132348.","journal-title":"International Journal of Computer Vision"},{"key":"2998_CR86","unstructured":"Zhou, Q., Pang, G., Tian, Y., He, S., & Chen, J. (2024a). Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection. In: ICLR."},{"key":"2998_CR87","doi-asserted-by":"publisher","first-page":"4401","DOI":"10.1007\/s11227-023-05625-1","volume":"80","author":"Y Zhou","year":"2024","unstructured":"Zhou, Y., Xiang, L., Liu, F., Duan, H., & Long, Y. (2024). Dynamic visual-guided selection for zero-shot learning. The Journal of Supercomputing, 80, 4401\u20134419.","journal-title":"The Journal of Supercomputing"},{"key":"2998_CR88","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2021). Deformable DETR: deformable transformers for end-to-end object detection. ICLR."},{"key":"2998_CR89","unstructured":"Zhu, Y., Xie, J., Tang, Z., Peng, X., & Elgammal, A. (2019). Semantic-guided multi-attention localization for zero-shot learning (pp. 14917\u201314927). NeurIPS."}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02998-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-026-02998-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02998-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T08:11:52Z","timestamp":1790151112000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-026-02998-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":89,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["2998"],"URL":"https:\/\/doi.org\/10.1007\/s11263-026-02998-7","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"9 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"419"}}