{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T17:03:21Z","timestamp":1784567001366,"version":"3.55.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62132002"],"award-info":[{"award-number":["62132002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["20250484786"],"award-info":[{"award-number":["20250484786"]}],"id":[{"id":"10.13039\/501100005090","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guizhou Provincial Major Scientifc and Technological Program","award":["Qiankehe Zhongda [2025] No. 032"],"award-info":[{"award-number":["Qiankehe Zhongda [2025] No. 032"]}]},{"name":"Fundamental Research Funds for Central Universities"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s11263-026-02891-3","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T17:14:32Z","timestamp":1779988472000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adapting Vision-Language Models from Iconic to Inclusive for Multi-label Recognition Without Labels"],"prefix":"10.1007","volume":"134","author":[{"given":"Cheng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5691-013X","authenticated-orcid":false,"given":"Yifan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"2891_CR1","unstructured":"Abdelfattah, R., Zhang, X., Fouda, MM., Wang, X., & Wang, S. (2022). G2netpl: Generic game-theoretic network for partial-label image classification. In: BMVC"},{"key":"2891_CR2","doi-asserted-by":"crossref","unstructured":"Abdelfattah, R., Guo, Q., Li, X., Wang, X., & Wang, S. (2023). Cdul: Clip-driven unsupervised learning for multi-label image classification. In: ICCV, 1348\u20131357","DOI":"10.1109\/ICCV51070.2023.00130"},{"key":"2891_CR3","doi-asserted-by":"crossref","unstructured":"Ben-Baruch, E., Ridnik, T., Friedman, I., Ben-Cohen, A., Zamir, N., & Noy, A. (2022). Zelnik-Manor L Multi-label classification with partial annotations using class-aware selective loss (pp. 4764\u20134772). CVPR.","DOI":"10.1109\/CVPR52688.2022.00472"},{"key":"2891_CR4","unstructured":"Chen, C., Zhao, Y., & Li, J. (2023a). Semantic contrastive bootstrapping for single-positive multi-label recognition. IJCV pp. 1\u201318."},{"key":"2891_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Pu, T., Wu, H., Xie, Y., & Lin, L. (2022). Structured semantic transfer for multi-label recognition with partial labels. AAAI.","DOI":"10.1609\/aaai.v36i1.19910"},{"key":"2891_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, F., Wang, H., Wang, C., Liu, Y., Tian, Y., & Carneiro, G. (2023b). Bomd: Bag of multi-label descriptors for noisy chest x-ray classification. In: ICCV, pp. 21284\u201321295.","DOI":"10.1109\/ICCV51070.2023.01946"},{"key":"2891_CR7","doi-asserted-by":"crossref","unstructured":"Chua, T. S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y. T. (2009). Nus-wide: A real-world web image database from national university of singapore. In: CIVR, Santorini, Greece.","DOI":"10.1145\/1646396.1646452"},{"key":"2891_CR8","doi-asserted-by":"crossref","unstructured":"Cole, E., Aodha, M., Lorieul, O., Perona, T., Morris, P., Jojic, D. (2021). Multi-label learning from single positive labels (pp. 933\u2013942). CVPR.","DOI":"10.1109\/CVPR46437.2021.00099"},{"key":"2891_CR9","doi-asserted-by":"crossref","unstructured":"Durand, T., Mehrasa, N., & Mori, G. (2019). Learning a deep convnet for multi-label classification with partial labels. CVPR.","DOI":"10.1109\/CVPR.2019.00074"},{"key":"2891_CR10","unstructured":"Everingham, M., Van Gool, L., Williams, CKI., Winn, J., & Zisserman, A. (2007). The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2007\/workshop\/index.html"},{"key":"2891_CR11","unstructured":"Everingham, M., Van Gool, L., Williams, CKI., Winn, J., & Zisserman, A. (2012). The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html"},{"key":"2891_CR12","unstructured":"Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., & Qiao, Y. (2021). Clip-adapter: Better vision-language models with feature adapters. 2110.04544"},{"key":"2891_CR13","doi-asserted-by":"crossref","unstructured":"Guo, Z., Dong, B., Ji, Z., Bai, J., Guo, Y., & Zuo, W. (2023). Texts as images in prompt tuning for multi-label image recognition. In: CVPR, pp. 2808\u20132817.","DOI":"10.1109\/CVPR52729.2023.00275"},{"key":"2891_CR14","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778, https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2891_CR15","doi-asserted-by":"crossref","unstructured":"Jo, Y., Kim, J., & Park, J. (2025). Bac-gcn: Background-aware clip-gcn framework for unsupervised multi-label classification. ACM MM pp. 3942\u20133951.","DOI":"10.1145\/3746027.3755253"},{"key":"2891_CR16","doi-asserted-by":"crossref","unstructured":"Kim, D., & Shim, H. (2025). Classifier-guided clip distillation for unsupervised multi-label classification pp. 4661\u20134671. CVPR.","DOI":"10.1109\/CVPR52734.2025.00439"},{"key":"2891_CR17","doi-asserted-by":"crossref","unstructured":"Kim, Y., Kim, JM., Akata, Z., & Lee, J. (2022). Large loss matters in weakly supervised multi-label classification. In: CVPR, pp. 14156\u201314165.","DOI":"10.1109\/CVPR52688.2022.01376"},{"key":"2891_CR18","doi-asserted-by":"crossref","unstructured":"Kim, Y., Kim, JM., Jeong, J., Schmid, C., Akata, Z., & Lee, J. (2023). Bridging the gap between model explanations in partially annotated multi-label classification. In: CVPR, pp. 3408\u20133417.","DOI":"10.1109\/CVPR52729.2023.00332"},{"key":"2891_CR19","unstructured":"Kipf, T. (2016). Semi-supervised classification with graph convolutional networks arXiv:1609.02907 arXiv preprint."},{"key":"2891_CR20","doi-asserted-by":"crossref","unstructured":"Lanchantin, J., Wang, T., Ordonez, V., & Qi, Y. (2021). General multi-label image classification with transformers pp. 16478\u201316488. CVPR.","DOI":"10.1109\/CVPR46437.2021.01621"},{"key":"2891_CR21","doi-asserted-by":"crossref","unstructured":"Lee, S., Lee, S., Seong, H., Kim, E. (2023). Revisiting self-similarity: Structural embedding for image retrieval. In: CVPR, pp. 23412\u201323421.","DOI":"10.1109\/CVPR52729.2023.02242"},{"key":"2891_CR22","unstructured":"Li, J., Li, D., Savarese, S., & Hoi, S. (2023a). Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In: International conference on machine learning, PMLR, pp. 19730\u201319742."},{"key":"2891_CR23","doi-asserted-by":"crossref","unstructured":"Li, L., Xiao, J., Chen, G., Shao, J., Zhuang, Y., & Chen, L. (2023b). Zero-shot visual relation detection via composite visual cues from large language models. In: NeurIPS, Curran Associates, Inc.","DOI":"10.52202\/075280-2179"},{"key":"2891_CR24","doi-asserted-by":"crossref","unstructured":"Li, M., Wang, D., Liu, X., Zeng, Z., Lu, R., Chen, B., & Zhou, M. (2023c). Patchct: Aligning patch set and label set with conditional transport for multi-label image classification. In: ICCV, pp. 15348\u201315358","DOI":"10.1109\/ICCV51070.2023.01408"},{"key":"2891_CR25","doi-asserted-by":"crossref","unstructured":"Liang, F., Wu, B., Dai, X., Li, K., Zhao, Y., Zhang, H., Zhang, P., Vajda, P., & Marculescu, D. (2023). Open-vocabulary semantic segmentation with mask-adapted clip. In: CVPR, pp. 7061\u20137070.","DOI":"10.1109\/CVPR52729.2023.00682"},{"key":"2891_CR26","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. ECCV.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2891_CR27","doi-asserted-by":"crossref","unstructured":"Lin, Y., Chen, M., Wang, W., Wu, B., Li, K., Lin, B., Liu, H., & He, X. (2023). Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation. In: CVPR, pp. 15305\u201315314.","DOI":"10.1109\/CVPR52729.2023.01469"},{"key":"2891_CR28","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.1609\/aaai.v38i4.28139","volume":"38","author":"Y Lin","year":"2024","unstructured":"Lin, Y., Chen, M., Zhang, K., Li, H., Li, M., Yang, Z., Lv, D., Lin, B., Liu, H., & Cai, D. (2024). Tagclip: A local-to-global framework to enhance open-vocabulary multi-label classification of clip without training. Proceedings of the AAAI Conference on Artificial Intelligence, 38, 3513\u20133521.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"2891_CR29","doi-asserted-by":"crossref","unstructured":"Liu, C., Zhang, W., Lin, X., Zhang, W., Tan, X., Han, J., Li, X., Ding, E., & Wang, J. (2023a). Ambiguity-resistant semi-supervised learning for dense object detection. In: CVPR, pp. 15579\u201315588.","DOI":"10.1109\/CVPR52729.2023.01495"},{"key":"2891_CR30","doi-asserted-by":"crossref","unstructured":"Liu, R., Liu, H., Li, G., Hou, H., Yu, T., & Yang, T. (2022). Contextual debiasing for visual recognition with causal mechanisms. In: CVPR, pp. 12755\u201312765.","DOI":"10.1109\/CVPR52688.2022.01242"},{"key":"2891_CR31","doi-asserted-by":"crossref","unstructured":"Liu, X., Tian, B., Wang, Z., Wang, R., Sheng, K., Zhang, B., Zhao, H., & Zhou, G. (2023b). Delving into shape-aware zero-shot semantic segmentation. In: CVPR, pp. 2999\u20133009.","DOI":"10.1109\/CVPR52729.2023.00293"},{"key":"2891_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Z., Guo, S., Lu, X., Guo, J., Zhang, J., Zeng, Y., & Huo, F. (2023c). (ml) $$^{2}$$ p-encoder: On exploration of channel-class correlation for multi-label zero-shot learning. In: CVPR, pp. 23859\u201323868.","DOI":"10.1109\/CVPR52729.2023.02285"},{"key":"2891_CR33","unstructured":"Loshchilov, I., & Hutter, F. (2019). Decoupled weight decay regularization. ICLR."},{"key":"2891_CR34","doi-asserted-by":"crossref","unstructured":"Ma, L. L., Xu, S., Xie, M. K., Wang, L., Sun, D., & Zhao, H. (2025). Correlative and discriminative label grouping for multi-label visual prompt tuning pp. 25434\u201325443. CVPR.","DOI":"10.1109\/CVPR52734.2025.02368"},{"key":"2891_CR35","doi-asserted-by":"crossref","unstructured":"Ma, Y., Li, H., Zhang, Z., Guo, J., Zhang, S., Gong, R., Liu, X. (2023). Annealing-based label-transfer learning for open world object detection. In: CVPR, pp. 11454\u201311463.","DOI":"10.1109\/CVPR52729.2023.01102"},{"key":"2891_CR36","doi-asserted-by":"crossref","unstructured":"Pathiraja, B., Gunawardhana, M., & Khan, M. H. (2023). Multiclass confidence and localization calibration for object detection pp. 19734\u201319743. CVPR.","DOI":"10.1109\/CVPR52729.2023.01890"},{"key":"2891_CR37","doi-asserted-by":"crossref","unstructured":"Pu, T., Chen, T., Wu, H., & Lin, L. (2022). Semantic-aware representation blending for multi-label image recognition with partial labels. AAAI.","DOI":"10.1609\/aaai.v36i2.20105"},{"key":"2891_CR38","unstructured":"Radford, A., Kim, JW., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In: Meila M, Zhang T (eds) ICML, PMLR, Proceedings of Machine Learning Research, vol. 139, pp. 8748\u20138763, https:\/\/proceedings.mlr.press\/v139\/radford21a.html"},{"key":"2891_CR39","doi-asserted-by":"crossref","unstructured":"Rajeswar, S., Rodr\u00edguez, P., Singhal, S., Vazquez, D., & Courville, A. (2022). Multi-label iterated learning for image classification with label ambiguity pp. 4783\u20134793. CVPR.","DOI":"10.1109\/CVPR52688.2022.00474"},{"key":"2891_CR40","doi-asserted-by":"crossref","unstructured":"Ramos, R., Martins, B., Elliott, D., & Kementchedjhieva, Y. (2023). Smallcap: Lightweight image captioning prompted with retrieval augmentation pp. 2840\u20132849. CVPR.","DOI":"10.1109\/CVPR52729.2023.00278"},{"key":"2891_CR41","doi-asserted-by":"crossref","unstructured":"Ridnik, T., Ben-Baruch, E., Zamir, N., Noy, A., Friedman, I., Protter, M., & Zelnik-Manor, L. (2021). Asymmetric loss for multi-label classification pp. 82\u201391. ICCV.","DOI":"10.1109\/ICCV48922.2021.00015"},{"key":"2891_CR42","doi-asserted-by":"crossref","unstructured":"Sain, A., Bhunia, A. K., Chowdhury, P. N., Koley, S., Xiang, T., & Song, Y. Z. (2023). Clip for all things zero-shot sketch-based image retrieval, fine-grained or not pp. 2765\u20132775. CVPR.","DOI":"10.1109\/CVPR52729.2023.00271"},{"key":"2891_CR43","doi-asserted-by":"crossref","unstructured":"Saito, K., Sohn, K., Zhang, X., Li, CL., Lee, CY., Saenko, K., & Pfister, T. (2023). Pic2word: Mapping pictures to words for zero-shot composed image retrieval. In: CVPR, pp. 19305\u201319314.","DOI":"10.1109\/CVPR52729.2023.01850"},{"key":"2891_CR44","unstructured":"Sohn, K., Berthelot, D., Li, CL., Zhang, Z., Carlini, N., Cubuk, ED., Kurakin, A., Zhang, H., & Raffel, C. (2020). Fixmatch: Simplifying semi-supervised learning with consistency and confidence. arXiv preprint arXiv:2001.07685"},{"key":"2891_CR45","doi-asserted-by":"crossref","unstructured":"Sun, X., Hu, P., & Saenko, K. (2022). Dualcoop: Fast adaptation to multi-label recognition with limited annotations. In: Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A (eds) NeurIPS, Curran Associates, Inc., vol.\u00a035, pp. 30569\u201330582, https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/c5169260ef32d1bd3597c14d8c89b034-Paper-Conference.pdf","DOI":"10.52202\/068431-2216"},{"key":"2891_CR46","doi-asserted-by":"crossref","unstructured":"Tan, H., Tan, Z., Li, J., Liu, A., Wan, J., & Lei, Z. (2025). Recover and match: Open-vocabulary multi-label recognition through knowledge-constrained optimal transport. In: CVPR, pp. 4650\u20134660.","DOI":"10.1109\/CVPR52734.2025.00438"},{"key":"2891_CR47","doi-asserted-by":"crossref","unstructured":"Wu, S., Zhang, W., Jin, S., Liu, W., & Loy, CC. (2023). Aligning bag of regions for open-vocabulary object detection. In: CVPR, pp. 15254\u201315264.","DOI":"10.1109\/CVPR52729.2023.01464"},{"key":"2891_CR48","doi-asserted-by":"crossref","unstructured":"Xia, X., Deng, J., Bao, W., Du, Y., Han, B., Shan, S., & Liu, T. (2023). Holistic label correction for noisy multi-label classification pp. 1483\u20131493. ICCV.","DOI":"10.1109\/ICCV51070.2023.00143"},{"key":"2891_CR49","doi-asserted-by":"crossref","unstructured":"Xie, C. W., Sun, S., Xiong, X., Zheng, Y., Zhao, D., & Zhou, J. (2023). Ra-clip: Retrieval augmented contrastive language-image pre-training pp. 19265\u201319274. CVPR.","DOI":"10.1109\/CVPR52729.2023.01846"},{"key":"2891_CR50","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhang, Z., Wei, F., Hu, H., & Bai, X. (2023). Side adapter network for open-vocabulary semantic segmentation pp. 2945\u20132954. CVPR.","DOI":"10.1109\/CVPR52729.2023.00288"},{"key":"2891_CR51","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Zhang, H., Lu, R., Wang, D., Chen, B., & Wang, Z. (2023). Conzic: Controllable zero-shot image captioning by sampling-based polishing. In: CVPR, pp. 23465\u201323476.","DOI":"10.1109\/CVPR52729.2023.02247"},{"key":"2891_CR52","unstructured":"Zhang, B., Wang, Y., Hou, W., WU, H., Wang, J., Okumura, M., & Shinozaki, T. (2021). Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang P, Vaughan JW (eds) NeurIPS, Curran Associates, Inc., 34, pp. 18408\u201318419, https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/995693c15f439e3d189b06e89d145dd5-Paper.pdf"},{"key":"2891_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, S., Xu, R., Xiong, C., & Ramaiah, C. (2022). Use all the labels: A hierarchical multi-label contrastive learning framework pp. 16660\u201316669. CVPR.","DOI":"10.1109\/CVPR52688.2022.01616"},{"key":"2891_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, W., Liu, C., Zeng, L., Ooi, B., Tang, S., & Zhuang, Y. (2023). Learning in imperfect environment: Multi-label classification with long-tailed distribution and partial labels pp. 1423\u20131432. ICCV.","DOI":"10.1109\/ICCV51070.2023.00137"},{"key":"2891_CR55","doi-asserted-by":"crossref","unstructured":"Zhao, H., Xu, S., Ma, L., Zhang, Y., Wang, L., & Sun, D. (2025). Towards space and semantics: Object-purified representation learning for multi-label image classification. ACM MM pp. 3270\u20133279.","DOI":"10.1145\/3746027.3754878"},{"key":"2891_CR56","doi-asserted-by":"crossref","unstructured":"Zhao J, Yan, K., Zhao, Y., Guo, X., Huang, F., & Li, J. (2021). Transformer-based dual relation graph for multi-label image recognition. In: ICCV, pp. 163\u2013172.","DOI":"10.1109\/ICCV48922.2021.00023"},{"key":"2891_CR57","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Yang, L., Long, S., Pi, J., Zhou, L., & Wang, J. (2023). Augmentation matters: A simple-yet-effective approach to semi-supervised semantic segmentation pp. 11350\u201311359. CVPR.","DOI":"10.1109\/CVPR52729.2023.01092"},{"key":"2891_CR58","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022). Conditional prompt learning for vision-language models pp. 16816\u201316825. CVPR.","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"2891_CR59","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Lei, Y., Zhang, B., Liu, L., & Liu, Y. (2023). Zegclip: Towards adapting clip for zero-shot semantic segmentation. In: CVPR, pp. 11175\u201311185","DOI":"10.1109\/CVPR52729.2023.01075"},{"key":"2891_CR60","doi-asserted-by":"crossref","unstructured":"Zhu, K., Fu, M., & Wu, J. (2023a). Multi-label self-supervised learning with scene images. In: ICCV, pp. 6694\u20136703.","DOI":"10.1109\/ICCV51070.2023.00616"},{"key":"2891_CR61","doi-asserted-by":"crossref","unstructured":"Zhu, X., Liu, J., Liu, W., Ge, J., Liu, B., & Cao, J. (2023b). Scene-aware label graph learning for multi-label image classification. In: ICCV, pp. 1473\u20131482.","DOI":"10.1109\/ICCV51070.2023.00142"},{"key":"2891_CR62","doi-asserted-by":"crossref","unstructured":"Zhu, X., Liu, J., Cao, J., & Wang, B. (2025). Mambaml: Exploring state space models for multi-label image classification pp. 4743\u20134753. CVPR.","DOI":"10.1109\/ICCV51701.2025.00451"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02891-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-026-02891-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02891-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:14:10Z","timestamp":1784564050000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-026-02891-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":62,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["2891"],"URL":"https:\/\/doi.org\/10.1007\/s11263-026-02891-3","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]},"assertion":[{"value":"4 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This work is partially supported by grants from the National Natural Science Foundation of China (No.62132002), Guizhou Provincial Major Scientific and Technological Program (Qiankehe Zhongda [2025] No. 032), Beijing Nova Program (No.20250484786), and the Fundamental Research Funds for the Central Universities.","order":1,"name":"Ethics","label":"Funding","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study does not involve experiments requiring informed consent from participants; therefore, this item is not applicable.","order":4,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have approved the final manuscript and consent to its publication.","order":5,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"294"}}