{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T09:08:12Z","timestamp":1784279292462,"version":"3.55.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":65,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076117"],"award-info":[{"award-number":["62076117"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangxi Provincial Key Laboratory of Virtual Reality","award":["2024SSY03151"],"award-info":[{"award-number":["2024SSY03151"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Channel-Spatial mixed attention methods have been proven to enhance the representation capability of convolutional neural networks. Most existing channel-spatial mixed attention methods decouple the channel domain and spatial domain, investing different orders of computational complexity in calculating attention for the two domains. However, both the channel domain and spatial domain are equally important and deserve comparable levels of complexity in attention computation. To solve this problem, we propose a novel channel-spatial mixed attention method, which is called\n                    <jats:bold>D<\/jats:bold>\n                    ual-domain\n                    <jats:bold>B<\/jats:bold>\n                    alanced\n                    <jats:bold>C<\/jats:bold>\n                    hannel-\n                    <jats:bold>S<\/jats:bold>\n                    patial\n                    <jats:bold>M<\/jats:bold>\n                    ixed\n                    <jats:bold>A<\/jats:bold>\n                    ttention (DBCSMA). The input tensor is first decomposed into channel and spatial domains using global pooling. Then, the same order of complexity is dedicated to calculating attention in both the channel domain and the spatial domain, and a mixed attention tensor is generated using multiplicative aggregation. Finally, input channel information and spatial information are simultaneously recalibrated using multiplication of this mixed attention tensor and the input tensor. Experimental results demonstrate that DBCSMA achieves superior performance compared to most existing channel-spatial mixed attention methods while using lower complexity.\n                  <\/jats:p>","DOI":"10.1007\/s11063-026-11855-0","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T15:53:32Z","timestamp":1778687612000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-Domain Balanced Channel-Spatial Mixed Attention in Convolutional Neural Networks"],"prefix":"10.1007","volume":"58","author":[{"given":"Meng","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidong","family":"Min","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shimiao","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixin","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangrong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiongjin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"issue":"6","key":"11855_CR1","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"11855_CR2","doi-asserted-by":"crossref","unstructured":"Toshev A, Szegedy C (2014) Deeppose: Human pose estimation via deep neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1653\u20131660","DOI":"10.1109\/CVPR.2014.214"},{"key":"11855_CR3","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"11855_CR4","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY et al (2018) Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision, pp. 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"11855_CR5","unstructured":"Park J, Woo S, Lee JY et al (2018) Bam: Bottleneck attention module. In: Proceedings of the British Machine Vision Conference, pp. 1\u201314"},{"key":"11855_CR6","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"11855_CR7","doi-asserted-by":"publisher","unstructured":"Ouyang D, He S, Zhang G et al (2023) Efficient multi-scale attention module with cross-spatial learning. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1\u20135. https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10096516","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"11855_CR8","doi-asserted-by":"crossref","unstructured":"Wang C, Zhang Q, Huang C et al (2018) Mancs: A multi-task attentional network with curriculum sampling for person re-identification. In: Proceedings of the European Conference on Computer Vision, pp. 365\u2013381","DOI":"10.1007\/978-3-030-01225-0_23"},{"key":"11855_CR9","unstructured":"Linsley D, Shiebler D, Eberhardt S, Serre T (2019) Learning what and where to attend with humans in the loop. In: Proceedings of the International Conference on Learning Representations, pp. 1\u201321"},{"key":"11855_CR10","doi-asserted-by":"crossref","unstructured":"Misra D, Nalamada T, Arasanipalai AU, Hou Q (2021) Rotate to attend: Convolutional triplet attention module. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3139\u20133148","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"11855_CR11","doi-asserted-by":"publisher","unstructured":"Zhang QL, Yang YB (2021) Sa-net: Shuffle attention for deep convolutional neural networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2235\u20132239. https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9414568","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"11855_CR12","first-page":"11863","volume":"139","author":"L Yang","year":"2021","unstructured":"Yang L, Zhang RY, Li L, Xie X (2021) Simam: A simple, parameter-free attention module for convolutional neural networks. Proceedings of the International Conference on Machine Learning 139:11863\u201311874","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"11855_CR13","unstructured":"Krizhevsky A, Hinton GE (2009) Learning Multiple Layers of Features from Tiny Images. https:\/\/citeseerx.ist.psu.edu"},{"key":"11855_CR14","unstructured":"Everingham M, Winn J (2011) The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Analysis, Statistical Modelling and Computational Learning 8"},{"key":"11855_CR15","doi-asserted-by":"crossref","unstructured":"Chen L, Zhang H, Xiao J et al (2017) Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5659\u20135667","DOI":"10.1109\/CVPR.2017.667"},{"key":"11855_CR16","doi-asserted-by":"crossref","unstructured":"Wang F, Jiang M, Qian C et al (2017) Residual attention network for image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3156\u20133164","DOI":"10.1109\/CVPR.2017.683"},{"key":"11855_CR17","first-page":"6105","volume":"97","author":"M Tan","year":"2019","unstructured":"Tan M, Le QV (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning 97:6105\u20136114","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"11855_CR18","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11855_CR19","unstructured":"Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: Proceedings of the International Conference on Machine Learning, pp. 1\u201315"},{"key":"11855_CR20","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G et al (2019) Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"11855_CR21","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R et al (2017) Focal loss for dense object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"11855_CR22","first-page":"249","volume":"9","author":"X Glorot","year":"2010","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Proceedings of the International Conference on Artificial Intelligence and Statistics 9:249\u2013256","journal-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics"},{"key":"11855_CR23","first-page":"448","volume":"37","author":"S Ioffe","year":"2015","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning 37:448\u2013456","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"11855_CR24","unstructured":"Paszke A, Gross S, Massa F et al (2019) Pytorch: An imperative style, high-performance deep learning library. In: Proceedings of the International Conference on Neural Information Processing Systems, pp. 8026\u20138037"},{"issue":"8","key":"11855_CR25","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, Shen L, Albanie S et al (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011\u20132023. https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11855_CR26","doi-asserted-by":"publisher","unstructured":"Chattopadhay A, Sarkar A, Howlader P et al (2018) Grad-cam plus plus : Generalized gradient-based visual explanations for deep convolutional networks. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 839\u2013847. https:\/\/doi.org\/10.1109\/WACV.2018.00097","DOI":"10.1109\/WACV.2018.00097"},{"key":"11855_CR27","doi-asserted-by":"publisher","unstructured":"Zhu M, Min W, Han Q et al (2024) Shufflenemt: modern lightweight convolutional neural network architecture. Pattern Analysis and Applications 27(4). https:\/\/doi.org\/10.1007\/s10044-024-01327-3","DOI":"10.1007\/s10044-024-01327-3"},{"key":"11855_CR28","doi-asserted-by":"publisher","unstructured":"Rossi RA, Ahmed NK, Koh E (2018) Higher-order network representation learning. In: Proceedings of the World Wide Web Conference, pp. 3\u20134. https:\/\/doi.org\/10.1145\/3184558.3186900","DOI":"10.1145\/3184558.3186900"},{"key":"11855_CR29","doi-asserted-by":"publisher","unstructured":"Fan W, Ma Y, Li Q et al (2019) Graph neural networks for social recommendation. In: Proceedings of the World Wide Web Conference, pp. 417\u2013426. https:\/\/doi.org\/10.1145\/3308558.3313488","DOI":"10.1145\/3308558.3313488"},{"key":"11855_CR30","doi-asserted-by":"publisher","unstructured":"Mandal S, Maiti A (2021) Graph neural networks for heterogeneous trust based social recommendation. In: Proceedings of the International Joint Conference on Neural Networks. https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9533367","DOI":"10.1109\/IJCNN52387.2021.9533367"},{"key":"11855_CR31","doi-asserted-by":"crossref","unstructured":"Dong X, Yu L, Wu Z et al (2017) A hybrid collaborative filtering model with deep structure for recommender systems. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1309\u20131315","DOI":"10.1609\/aaai.v31i1.10747"},{"issue":"11","key":"11855_CR32","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1109\/TKDE.2017.2741484","volume":"29","author":"H Yin","year":"2017","unstructured":"Yin H, Wang W, Wang H et al (2017) Spatial-aware hierarchical collaborative deep learning for poi recommendation. IEEE Trans Knowl Data Eng 29(11):2537\u20132551. https:\/\/doi.org\/10.1109\/TKDE.2017.2741484","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"11","key":"11855_CR33","doi-asserted-by":"publisher","first-page":"7855","DOI":"10.1007\/s10489-020-02162-9","volume":"51","author":"S Mandal","year":"2021","unstructured":"Mandal S, Maiti A (2021) Deep collaborative filtering with social promoter score-based user-item interaction: A new perspective in recommendation. Appl Intell 51(11):7855\u20137880. https:\/\/doi.org\/10.1007\/s10489-020-02162-9","journal-title":"Appl Intell"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-026-11855-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-026-11855-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-026-11855-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T08:32:58Z","timestamp":1784277178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-026-11855-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":33,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["11855"],"URL":"https:\/\/doi.org\/10.1007\/s11063-026-11855-0","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,13]]},"assertion":[{"value":"27 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflicts of Interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"48"}}