{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:31:18Z","timestamp":1767141078559,"version":"build-2238731810"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"21-22","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s00500-025-10915-2","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T10:50:02Z","timestamp":1758883802000},"page":"5845-5857","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An asymmetric lightweight multi-task network based on KAN convolution"],"prefix":"10.1007","volume":"29","author":[{"given":"Fanyun","family":"Meng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7680-552X","authenticated-orcid":false,"given":"Yongqiang","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhenyu","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Liping","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Jinlong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"10915_CR1","unstructured":"Alexey D: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv: 201011929 2020."},{"issue":"12","key":"10915_CR2","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10915_CR3","first-page":"129","volume":"2","author":"D Blalock","year":"2020","unstructured":"Blalock D, Gonzalez Ortiz JJ, Frankle J, Guttag J (2020) What is the state of neural network pruning? Proceedings of Machine Learning and Systems 2:129\u2013146","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"10915_CR4","unstructured":"Chen T, Zhang Z, Jaiswal A, Liu S, Wang Z: Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers.(2023). arXiv preprint csLG\/230301610 2023."},{"key":"10915_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110606","volume":"154","author":"S Deng","year":"2024","unstructured":"Deng S, Yang G, Yang Y et al (2024) Module-based graph pooling for graph classification. Pattern Recogn 154:110606","journal-title":"Pattern Recogn"},{"key":"10915_CR6","unstructured":"Drokin I: Kolmogorov-Arnold Convolutions: Design Principles and Empirical Studies. arXiv preprint arXiv:240701092 2024."},{"key":"10915_CR7","doi-asserted-by":"crossref","unstructured":"Fang G, Ma X, Song M, Mi MB, Wang X: Depgraph: Towards any structural pruning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition: 2023; 2023: 16091\u201316101.","DOI":"10.1109\/CVPR52729.2023.01544"},{"key":"10915_CR8","doi-asserted-by":"crossref","unstructured":"Fey M, Lenssen JE, Weichert F, M\u00fcller H: Splinecnn: Fast geometric deep learning with continuous b-spline kernels. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2018; 2018: 869\u2013877.","DOI":"10.1109\/CVPR.2018.00097"},{"key":"10915_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110812","volume":"156","author":"M Fiaz","year":"2024","unstructured":"Fiaz M, Noman M, Cholakkal H et al (2024) Guided-attention and gated-aggregation network for medical image segmentation. Pattern Recogn 156:110812","journal-title":"Pattern Recogn"},{"key":"10915_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109558","volume":"140","author":"X Hou","year":"2023","unstructured":"Hou X, Liu M, Zhang S, Wei P, Chen B (2023) CAnet: contextual information and spatial attention based network for detecting small defects in manufacturing industry. Pattern Recogn 140:109558","journal-title":"Pattern Recogn"},{"issue":"17","key":"10915_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/s23177466","volume":"23","author":"N Ji","year":"2023","unstructured":"Ji N, Dong H, Meng F, Pang L (2023) Semantic segmentation and depth estimation based on residual attention mechanism. Sensors (Basel) 23(17):7466","journal-title":"Sensors (Basel)"},{"key":"10915_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.111423","volume":"162","author":"N Ji","year":"2025","unstructured":"Ji N, Sun Y, Meng F, Pang L, Tian Y (2025) Variable multi-scale attention fusion network and adaptive correcting gradient optimization for multi-task learning. Pattern Recogn 162:111423","journal-title":"Pattern Recogn"},{"key":"10915_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108159","volume":"121","author":"X Jin","year":"2022","unstructured":"Jin X, Xie Y, Wei X-S et al (2022) Delving deep into spatial pooling for squeeze-and-excitation networks. Pattern Recogn 121:108159","journal-title":"Pattern Recogn"},{"key":"10915_CR14","doi-asserted-by":"crossref","unstructured":"Lee Y, Kim J, Willette J, Hwang SJ: Mpvit: Multi-path vision transformer for dense prediction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition: 2022; 2022: 7287\u20137296.","DOI":"10.1109\/CVPR52688.2022.00714"},{"key":"10915_CR15","unstructured":"Lin B, Lin Z, Guo Y et al: Variational probabilistic fusion network for RGB-T semantic segmentation. arXiv preprint arXiv:230708536 2023."},{"key":"10915_CR16","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R et al: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2017; 2017: 2117\u20132125.","DOI":"10.1109\/CVPR.2017.106"},{"key":"10915_CR17","doi-asserted-by":"publisher","first-page":"2678","DOI":"10.1109\/TIP.2023.3272826","volume":"32","author":"D Liu","year":"2023","unstructured":"Liu D, Liang J, Geng T, Loui A, Zhou T (2023a) Tripartite feature enhanced pyramid network for dense prediction. IEEE Trans Image Process 32:2678\u20132692","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"10915_CR18","doi-asserted-by":"publisher","first-page":"3505","DOI":"10.1007\/s10489-022-03617-x","volume":"53","author":"Y Liu","year":"2023","unstructured":"Liu Y, Huang L, Li J et al (2023b) Multi-task learning based on geometric invariance discriminative features. Appl Intell 53(3):3505\u20133518","journal-title":"Appl Intell"},{"key":"10915_CR19","doi-asserted-by":"crossref","unstructured":"Liu S, Johns E, Davison AJ: End-to-end multi-task learning with attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition: 2019; 2019: 1871\u20131880.","DOI":"10.1109\/CVPR.2019.00197"},{"key":"10915_CR20","unstructured":"Liu Z, Wang Y, Vaidya S et al: Kan: Kolmogorov-arnold networks. arXiv preprint arXiv:240419756 2024."},{"key":"10915_CR21","doi-asserted-by":"crossref","unstructured":"Valanarasu JMJ, Oza P, Hacihaliloglu I, Patel VM: Medical transformer: Gated axial-attention for medical image segmentation. In: Medical image computing and computer assisted intervention\u2013MICCAI 2021: 24th international conference, Strasbourg, France, September 27\u2013October 1, 2021, proceedings, part I 24: 2021: Springer; 2021: 36\u201346.","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"10915_CR22","doi-asserted-by":"crossref","unstructured":"Misra I, Shrivastava A, Gupta A, Hebert M: Cross-stitch networks for multi-task learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition: 2016; 2016: 3994\u20134003.","DOI":"10.1109\/CVPR.2016.433"},{"key":"10915_CR23","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.neucom.2022.09.042","volume":"511","author":"ATM Nakamura","year":"2022","unstructured":"Nakamura ATM, Grassi V Jr, Wolf DF (2022) Leveraging convergence behavior to balance conflicting tasks in multi-task learning. Neurocomputing 511:43\u201353","journal-title":"Neurocomputing"},{"key":"10915_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18: 2015: Springer; 2015: 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10915_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108796","volume":"130","author":"R Qian","year":"2022","unstructured":"Qian R, Lai X, Li X (2022) 3D object detection for autonomous driving: a survey. Pattern Recogn 130:108796","journal-title":"Pattern Recogn"},{"issue":"6","key":"10915_CR26","doi-asserted-by":"publisher","first-page":"3205","DOI":"10.1109\/TNNLS.2022.3176493","volume":"34","author":"M Shi","year":"2022","unstructured":"Shi M, Shen J, Yi Q et al (2022) Lmffnet: a well-balanced lightweight network for fast and accurate semantic segmentation. IEEE Trans Neural Netw Learn Syst 34(6):3205\u20133219","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10915_CR27","doi-asserted-by":"crossref","unstructured":"Wang A, Chen H, Lin Z, Han J, Ding G: Repvit: Revisiting mobile cnn from vit perspective. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 2024; 2024: 15909\u201315920.","DOI":"10.1109\/CVPR52733.2024.01506"},{"key":"10915_CR28","doi-asserted-by":"crossref","unstructured":"Xing D, Shen J, Ho C, Tzes A: ROIFormer: semantic-aware region of interest transformer for efficient self-supervised monocular depth estimation. In: Proceedings of the AAAI Conference on Artificial Intelligence: 2023; 2023: 2983\u20132991.","DOI":"10.1609\/aaai.v37i3.25401"},{"key":"10915_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107798","volume":"112","author":"Y Yang","year":"2021","unstructured":"Yang Y, Qi Y (2021) Image super-resolution via channel attention and spatial graph convolutional network. Pattern Recogn 112:107798","journal-title":"Pattern Recogn"},{"key":"10915_CR30","first-page":"5824","volume":"33","author":"T Yu","year":"2020","unstructured":"Yu T, Kumar S, Gupta A et al (2020) Gradient surgery for multi-task learning. Adv Neural Inf Process Syst 33:5824\u20135836","journal-title":"Adv Neural Inf Process Syst"},{"key":"10915_CR31","doi-asserted-by":"crossref","unstructured":"Zhang H, Hu W, Wang X: Parc-net: Position aware circular convolution with merits from convnets and transformer. In: European conference on computer vision: 2022: Springer; 2022: 613\u2013630.","DOI":"10.1007\/978-3-031-19809-0_35"},{"key":"10915_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110148","volume":"148","author":"M Zhu","year":"2024","unstructured":"Zhu M, Min W, Han J, Han Q, Cui S (2024) Improved channel attention methods via hierarchical pooling and reducing information loss. Pattern Recogn 148:110148","journal-title":"Pattern Recogn"},{"key":"10915_CR33","doi-asserted-by":"crossref","unstructured":"Zhang G, Li Z, Tang C, Li J, Hu X: Cednet: A cascade encoder-decoder network for dense prediction. Pattern Recognition 2024:111072.","DOI":"10.2139\/ssrn.4857945"}],"updated-by":[{"DOI":"10.1007\/s00500-025-10960-x","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000}}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10915-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10915-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10915-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:30:01Z","timestamp":1764052201000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-025-10915-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,26]]},"references-count":33,"journal-issue":{"issue":"21-22","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10915"],"URL":"https:\/\/doi.org\/10.1007\/s00500-025-10915-2","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,26]]},"assertion":[{"value":"22 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2025","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The author name Liping Pang has been corrected in the original article.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2025","order":8,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":9,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":10,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00500-025-10960-x","URL":"https:\/\/doi.org\/10.1007\/s00500-025-10960-x","order":11,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}