{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T02:39:19Z","timestamp":1791686359716,"version":"4.3.4"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Siksha 'O' Anusandhan Deemed To Be University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-47008-5","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T01:05:10Z","timestamp":1775091910000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Meta-learned dynamic hierarchical fusion for robust multi-scale object classification"],"prefix":"10.1038","volume":"16","author":[{"given":"Prashanta Kumar","family":"Patra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amitav","family":"Mahapatra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"47008_CR1","doi-asserted-by":"publisher","first-page":"29237","DOI":"10.1038\/s41598-025-10075-1","volume":"15","author":"S Artabaz","year":"2025","unstructured":"Artabaz, S. & Sliman, L. Feature fusion and selection using handcrafted vs. deep learning methods for multimodal hand biometric recognition. Sci. Rep. 15, 29237 (2025).","journal-title":"Sci. Rep."},{"key":"47008_CR2","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s10791-025-09871-0","volume":"28","author":"B Wu","year":"2025","unstructured":"Wu, B. et al. A multilevel fine-grained feature fusion method for cross-modal image\u2013text retrieval. Discover Comput. 28, 327 (2025).","journal-title":"Discover Comput."},{"key":"47008_CR3","doi-asserted-by":"publisher","first-page":"7365","DOI":"10.3934\/era.2023373","volume":"31","author":"C Yang","year":"2023","unstructured":"Yang, C., Wang, J., Wei, S. & Yu, X. A feature fusion-based attention graph convolutional network for 3D classification and segmentation. Electron. Res. Arch. 31, 7365\u20137384 (2023).","journal-title":"Electron. Res. Arch."},{"key":"47008_CR4","doi-asserted-by":"publisher","first-page":"398","DOI":"10.3390\/act14080398","volume":"14","author":"H Luo","year":"2025","unstructured":"Luo, H. et al. A cross-modal multi-layer feature fusion meta-learning approach for fault diagnosis under class-imbalanced conditions. Actuators 14, 398 (2025).","journal-title":"Actuators"},{"key":"47008_CR5","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10462-024-10922-z","volume":"57","author":"AG Khoee","year":"2024","unstructured":"Khoee, A. G., Yu, Y. & Feldt, R. Domain generalization through meta-learning: a survey. Artif. Intell. Rev. 57, 285 (2024).","journal-title":"Artif. Intell. Rev."},{"key":"47008_CR6","unstructured":"Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In Proc. Int. Conf. Learn. Represent. (ICLR) (2017)."},{"key":"47008_CR7","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11063-024-11567-3","volume":"56","author":"L Wu","year":"2024","unstructured":"Wu, L. et al. Multi-modal domain adaptation method based on parameter fusion and two-step alignment. Neural Process. Lett. 56, 107 (2024).","journal-title":"Neural Process. Lett."},{"key":"47008_CR8","doi-asserted-by":"publisher","first-page":"103029","DOI":"10.1016\/j.inffus.2025.103029","volume":"119","author":"Z Qiao","year":"2025","unstructured":"Qiao, Z. et al. Enhancing cross-domain generalization by fusing language-guided feature remapping. Inf. Fusion. 119, 103029 (2025).","journal-title":"Inf. Fusion"},{"key":"47008_CR9","first-page":"1","volume":"57","author":"X Li","year":"2024","unstructured":"Li, X., Chen, Z., Wang, Y. & Zhang, Y. Meta-learning for domain generalization: a survey. Artif. Intell. Rev. 57, 1\u201338 (2024).","journal-title":"Artif. Intell. Rev."},{"key":"47008_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.32604\/cmc.2024.053204","volume":"80","author":"T Jiao","year":"2024","unstructured":"Jiao, T., Guo, C., Feng, X., Chen, Y. & Song, J. A comprehensive survey on deep learning multi\u2013modal fusion: methods, technologies and applications. Comput. Mater. Continua. 80, 1\u201335 (2024).","journal-title":"Comput. Mater. Continua"},{"key":"47008_CR11","unstructured":"Li, S. & Tang, H. Multimodal alignment and fusion: a survey. arXiv preprint arXiv:2411.17040 [cs.CV] (2024)."},{"key":"47008_CR12","first-page":"1","volume":"56","author":"F Zhao","year":"2024","unstructured":"Zhao, F., Zhang, C. & Geng, B. Deep multimodal data fusion. ACM Comput. Surv. 56, 1\u201336 (2024).","journal-title":"ACM Comput. Surv."},{"key":"47008_CR13","first-page":"2211","volume":"12","author":"M G\u00f6nen","year":"2011","unstructured":"G\u00f6nen, M. & Alpayd\u0131n, E. Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211\u20132268 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"47008_CR14","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 7132\u20137141 (2018).","DOI":"10.1109\/CVPR.2018.00745"},{"key":"47008_CR15","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J. Y. & Kweon, I. S. CBAM: Convolutional block attention module. In Proc. Eur. Conf. Comput. Vis. (ECCV), 3\u201319 (2018).","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"47008_CR16","unstructured":"Vaswani, A. et al. Attention is all you need. In Adv. Neural Inf. Process. Syst., 30 (2017)."},{"key":"47008_CR17","unstructured":"Dosovitskiy, A. et al. An image is worth 16\u00d716 words: Transformers for image recognition at scale. In Proc. Int. Conf. Learn. Represent. (ICLR) (2021)."},{"key":"47008_CR18","doi-asserted-by":"crossref","unstructured":"Tian, Q. et al. Meta-learning for domain generalization on graphs. In Proc. 31st ACM SIGKDD Conf. Knowl. Discov. Data Mining, Vol. 1, 1361\u20131372 (2025).","DOI":"10.1145\/3690624.3709188"},{"key":"47008_CR19","doi-asserted-by":"publisher","first-page":"2235","DOI":"10.1109\/JBHI.2024.3352970","volume":"28","author":"J Morano","year":"2024","unstructured":"Morano, J., Aresta, G., Grechenig, C., Schmidt-Erfurth, U. & Bogunovi\u0107, H. Deep multimodal fusion of data with heterogeneous dimensionality via projective networks. IEEE J. Biomed. Health Inf. 28, 2235\u20132246 (2024).","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"47008_CR20","unstructured":"Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. In Proc. Int. Conf. Mach. Learn. (ICML), 1126\u20131135 (2017)."},{"key":"47008_CR21","unstructured":"Nichol, A., Achiam, J. & Schulman, J. On first-order meta-learning algorithms. arXiv preprint (2018). arXiv:1803.02999 [cs.LG]."},{"key":"47008_CR22","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y. & Hospedales, T. Learning to generalize: Meta-learning for domain generalization. In Proc. AAAI Conf. Artif. Intell., 32, 1432\u20131440 (2018).","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"47008_CR23","doi-asserted-by":"publisher","first-page":"398","DOI":"10.3390\/act14080398","volume":"14","author":"H Luo","year":"2025","unstructured":"Luo, H. et al. A cross-modal multi-layer feature fusion meta-learning approach for fault diagnosis under class-imbalanced conditions. Actuators 14, 398 (2025).","journal-title":"Actuators"},{"key":"47008_CR24","doi-asserted-by":"publisher","first-page":"109352","DOI":"10.1016\/j.compchemeng.2025.109352","volume":"203","author":"H Ali","year":"2025","unstructured":"Ali, H. et al. Process monitoring and dynamic fusion of complex industrial systems: A reconstruction-based Bayesian framework. Comput. Chem. Eng. 203, 109352 (2025).","journal-title":"Comput. Chem. Eng."},{"key":"47008_CR25","doi-asserted-by":"publisher","first-page":"12411","DOI":"10.1038\/s41598-024-63195-5","volume":"14","author":"Z Yuan","year":"2024","unstructured":"Yuan, Z. et al. Multiscale knowledge distillation with attention-based fusion for robust human activity recognition. Sci. Rep. 14, 12411 (2024).","journal-title":"Sci. Rep."},{"key":"47008_CR26","doi-asserted-by":"publisher","first-page":"13373","DOI":"10.1038\/s41598-024-64041-4","volume":"14","author":"G Li","year":"2024","unstructured":"Li, G. et al. Multistage feature fusion knowledge distillation. Sci. Rep. 14, 13373 (2024).","journal-title":"Sci. Rep."},{"key":"47008_CR27","doi-asserted-by":"publisher","first-page":"11756","DOI":"10.1038\/s41598-024-62749-x","volume":"14","author":"W Gan","year":"2024","unstructured":"Gan, W. et al. Convolutional MLP orthogonal fusion of multiscale features for visual place recognition. Sci. Rep. 14, 11756 (2024).","journal-title":"Sci. Rep."},{"key":"47008_CR28","first-page":"108844","volume":"134","author":"K Wang","year":"2024","unstructured":"Wang, K., Zeng, H., Zhou, Y. & Huang, L. MTFR: An universal multimodal fusion method through modality transfer and fusion refinement. Eng. Appl. Artif. Intell. 134, 108844 (2024).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"47008_CR29","doi-asserted-by":"crossref","unstructured":"Ahmed, M. W. et al. SiWformer: Multi-scale feature fusion via discrete wavelet transform and vision transformer. Signal Image Video Process., 19, 1397 (2025).","DOI":"10.1007\/s11760-025-04894-y"},{"key":"47008_CR30","unstructured":"Krizhevsky, A. Learning multiple layers of features from tiny images. University of Toronto Technical Report (2009)."},{"key":"47008_CR31","doi-asserted-by":"crossref","unstructured":"Deng, J. et al. ImageNet: A large-scale hierarchical image database. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 248\u2013255 (2009).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"47008_CR32","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 Proc. IEEE Comput. Vis. Pattern Recognit. Workshop (2004)."},{"key":"47008_CR33","doi-asserted-by":"crossref","unstructured":"Krause, J. et al. 3D object representations for fine-grained categorization. In Proc. IEEE Int. Conf. Comput. Vis. Workshops, 554\u2013561 (2013).","DOI":"10.1109\/ICCVW.2013.77"},{"key":"47008_CR34","doi-asserted-by":"crossref","unstructured":"Parkhi, O. M. et al. Cats and dogs. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 3498\u20133505 (2012).","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"47008_CR35","doi-asserted-by":"crossref","unstructured":"Liu, R. Pedestrian detection based on SENet with attention mechanism. In Proc. IEEE 7th Inf. Technol. Mechatronics Eng. Conf. (ITOEC), 616\u2013619 (2023).","DOI":"10.1109\/ITOEC57671.2023.10291367"},{"key":"47008_CR36","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, W., Li, W. & Ke, X. D. G. A. T. Dynamic Graph Attention-Transformer network for battery state of health multi-step prediction. Energy, 136876 (2025).","DOI":"10.1016\/j.energy.2025.136876"},{"key":"47008_CR37","doi-asserted-by":"publisher","first-page":"4508","DOI":"10.3390\/s23094508","volume":"23","author":"J Dang","year":"2023","unstructured":"Dang, J., Tang, X. & Li, S. HA-FPN: Hierarchical attention feature pyramid network for object detection. Sensors 23, 4508 (2023).","journal-title":"Sensors"},{"key":"47008_CR38","doi-asserted-by":"crossref","unstructured":"Dulal, R., Zheng, L. & Kabir, M. A. MHAFF: Multihead Attention Feature Fusion of CNN and Transformer for Cattle Identification. IEEE Trans. AgriFood Electron. (2025).","DOI":"10.1109\/TAFE.2025.3574708"},{"key":"47008_CR39","doi-asserted-by":"crossref","unstructured":"Jha, A., Bose, S. & Banerjee, B. GAF-Net: Improving the performance of remote sensing image fusion using novel global self and cross attention learning. In Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis., 6354\u20136363 (2023).","DOI":"10.1109\/WACV56688.2023.00629"},{"key":"47008_CR40","unstructured":"Kandakji, L., Woof, W. & Pontikos, N. Hierarchical attention for sparse volumetric anomaly detection in subclinical keratoconus. arXiv preprint (2025). arXiv:2512.03346 [cs.CV]."},{"key":"47008_CR41","doi-asserted-by":"publisher","first-page":"2071","DOI":"10.1109\/TPAMI.2024.3523364","volume":"47","author":"X Wu","year":"2025","unstructured":"Wu, X. et al. Fully-connected transformer for multi-source image fusion. IEEE Trans. Pattern Anal. Mach. Intell. 47, 2071\u20132088 (2025).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"47008_CR42","doi-asserted-by":"crossref","unstructured":"Li, C., Li, T., Hu, X., Luo, D. & Jin, T. D. V. H. G. N. N. Multi-Scale Dilated Vision HGNN for Efficient Vision Recognition. In Proc. Comput. Vis. Pattern Recognit., 20158\u201320168 (2025).","DOI":"10.1109\/CVPR52734.2025.01877"},{"key":"47008_CR43","doi-asserted-by":"publisher","first-page":"4853","DOI":"10.3390\/rs15194853","volume":"15","author":"K Chen","year":"2023","unstructured":"Chen, K. et al. Multi-scale strip feature attention network for cloud and cloud shadow segmentation. Remote Sens. 15, 4853 (2023).","journal-title":"Remote Sens."},{"key":"47008_CR44","doi-asserted-by":"publisher","first-page":"1510252","DOI":"10.3389\/fcomp.2025.1510252","volume":"7","author":"L Triyono","year":"2025","unstructured":"Triyono, L., Gernowo, R., Prayitno, P. & MoNetViT An efficient fusion of CNN and transformer technologies for visual navigation assistance with multi query attention. Front. Comput. Sci. 7, 1510252 (2025).","journal-title":"Front. Comput. Sci."},{"key":"47008_CR45","doi-asserted-by":"publisher","first-page":"3883","DOI":"10.1007\/s00371-024-03637-3","volume":"41","author":"X Xie","year":"2025","unstructured":"Xie, X. et al. Toward high-fidelity end-to-end multi-focus image fusion via Swin transformer-based network. Vis. Comput. 41, 3883\u20133906 (2025).","journal-title":"Vis. Comput."},{"key":"47008_CR46","doi-asserted-by":"publisher","first-page":"7023","DOI":"10.3390\/s24217023","volume":"24","author":"R Li","year":"2024","unstructured":"Li, R., Zhang, L., Wang, Z. & Li, X. FCSwinU: Fourier convolutions and Swin transformer UNet for hyperspectral and multispectral image fusion. Sensors 24, 7023 (2024).","journal-title":"Sensors"},{"key":"47008_CR47","unstructured":"Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. In Proc. Int. Conf. Mach. Learn., 1126\u20131135 (2017)."},{"key":"47008_CR48","unstructured":"Balaji, Y., Sankaranarayanan, S., Chellappa, R. & MetaReg Towards domain generalization using meta-regularization. In Adv. Neural Inf. Process. Syst., 31 (2018)."},{"key":"47008_CR49","unstructured":"Ajakan, H., Germain, P., Larochelle, H., Laviolette, F. & Marchand, M. Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446 [cs.LG] (2014)."},{"key":"47008_CR50","unstructured":"gomagoti DSC_0346 [Photograph]. Wikimedia Commons. (2013). Available at: https:\/\/commons.wikimedia.org\/wiki\/File:German_Shepherd_-_DSC_0346_(10096362833).jpg ((Accessed: 12 March 2026).). Licensed under CC BY-SA 2.5."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47008-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47008-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47008-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T22:30:34Z","timestamp":1779229834000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47008-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["47008"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-47008-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]},"assertion":[{"value":"23 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"15613"}}