{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T10:17:55Z","timestamp":1768472275536,"version":"3.49.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Natural Science Foundation of China Enterprise Innovation and Development Joint Fund","award":["U19B2004"],"award-info":[{"award-number":["U19B2004"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10489-023-04963-0","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T08:45:10Z","timestamp":1695113110000},"page":"27935-27950","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive local recalibration network for scene recognition"],"prefix":"10.1007","volume":"53","author":[{"given":"Jiale","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lian","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cien","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liqiong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mofan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hu","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,19]]},"reference":[{"key":"4963_CR1","first-page":"2014","volume":"27","author":"B Zhou","year":"2014","unstructured":"Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. Advances in Neural Information Processing Systems (NIPS) 27:2014","journal-title":"Advances in Neural Information Processing Systems (NIPS)"},{"key":"4963_CR2","doi-asserted-by":"crossref","unstructured":"Liu T, Wang J, Yang B, Wang X (2021) Ngdnet: Nonuniform gaussian-label distribution learning for infrared head pose estimation and ontask behavior understanding in the classroom. Neurocomputing, 436:210\u2013220","DOI":"10.1016\/j.neucom.2020.12.090"},{"key":"4963_CR3","doi-asserted-by":"crossref","unstructured":"Li Z, Liu H, Zhang Z, Liu T, Xiong NN (2021) Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Trans Neural Netw Learn Syst 33(8):3961\u20133973","DOI":"10.1109\/TNNLS.2021.3055147"},{"key":"4963_CR4","doi-asserted-by":"crossref","unstructured":"H Liu, C Zheng, D Li, X Shen, K Lin, J Wang, Z Zhang, Z Zhang, NN Xiong. Edmf: Efficient deep matrix factorization with review feature learning for industrial recommender system. IEEE Transactions on Industrial Informatics, 18(7):4361\u20134371, 2021","DOI":"10.1109\/TII.2021.3128240"},{"key":"4963_CR5","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang L, Wang Y, Zhang B, Qiao Y (2017) Weakly supervised patchnets: Describing and aggregating local patches for scene recognition. IEEE Trans Image Process 26(4):2028\u20132041","DOI":"10.1109\/TIP.2017.2666739"},{"key":"4963_CR6","doi-asserted-by":"crossref","unstructured":"Wu R, Wang B, Wang W, Yu Y (2015) Harvesting discriminative meta objects with deep cnn features for scene classification. In Proceedings of the IEEE International Conference on Computer Vision, pages 1287\u20131295","DOI":"10.1109\/ICCV.2015.152"},{"key":"4963_CR7","doi-asserted-by":"crossref","unstructured":"Cheng X, Lu J, Feng J, Yuan B, Zhou J (2018) Scene recognition with objectness. Pattern Recognition 74:474\u2013487","DOI":"10.1016\/j.patcog.2017.09.025"},{"key":"4963_CR8","doi-asserted-by":"crossref","unstructured":"Zhao Z and Larson M (2018) From volcano to toyshop: Adaptive discriminative region discovery for scene recognition. In Proceedings of the 26th ACM international conference on Multimedia, pages 1760\u20131768","DOI":"10.1145\/3240508.3240698"},{"key":"4963_CR9","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"key":"4963_CR10","doi-asserted-by":"crossref","unstructured":"Simon M and Rodner E (2015) Neural activation constellations: Unsupervised part model discovery with convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 1143\u20131151","DOI":"10.1109\/ICCV.2015.136"},{"key":"4963_CR11","doi-asserted-by":"crossref","unstructured":"Song X, Jiang S, Herranz L (2017) Multi-scale multi-feature context modeling for scene recognition in the semantic manifold. IEEE Transactions on Image Processing, 26(6):2721\u20132735","DOI":"10.1109\/TIP.2017.2686017"},{"key":"4963_CR12","doi-asserted-by":"crossref","unstructured":"Zeng H, Song X, Chen G, Jiang S (2019) Learning scene attribute for scene recognition. IEEE Transactions on Multimedia 22(6):1519\u20131530","DOI":"10.1109\/TMM.2019.2944241"},{"key":"4963_CR13","doi-asserted-by":"crossref","unstructured":"Yu L, Jin M, Zhou K (2020) Multi-channel biomimetic visual transformation for object feature extraction and recognition of complex scenes. Applied Intelligence 50(3):792\u2013811","DOI":"10.1007\/s10489-019-01550-0"},{"key":"4963_CR14","doi-asserted-by":"crossref","unstructured":"Patterson G, Hays J (2012) Sun attribute database: Discovering, annotating, and recognizing scene attributes. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2751\u20132758. IEEE","DOI":"10.1109\/CVPR.2012.6247998"},{"key":"4963_CR15","doi-asserted-by":"crossref","unstructured":"Patterson G, Xu C, Su H, Hays J (2014) The sun attribute database: Beyond categories for deeper scene understanding. International Journal of Computer Vision, 108(1-2):59\u201381","DOI":"10.1007\/s11263-013-0695-z"},{"key":"4963_CR16","doi-asserted-by":"crossref","unstructured":"Wang L, Guo S, Huang W, Xiong Y, Qiao Y (2017) Knowledge guided disambiguation for large-scale scene classification with multiresolution cnns. IEEE Transactions on Image Processing 26(4):2055\u20132068","DOI":"10.1109\/TIP.2017.2675339"},{"key":"4963_CR17","doi-asserted-by":"crossref","unstructured":"Gao BB, Xing C, Xie CW, Wu J, Geng X (2017) Deep label distribution learning with label ambiguity. IEEE Transactions on Image Processing, 26(6):2825\u20132838","DOI":"10.1109\/TIP.2017.2689998"},{"key":"4963_CR18","doi-asserted-by":"crossref","unstructured":"Tanaka D, Ikami D, Yamasaki T, Aizawa K (2018) Joint optimization framework for learning with noisy labels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5552\u20135560","DOI":"10.1109\/CVPR.2018.00582"},{"key":"4963_CR19","doi-asserted-by":"crossref","unstructured":"Yi K, Wu J (2019) Probabilistic end-to-end noise correction for learning with noisy labels. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 7017\u20137025","DOI":"10.1109\/CVPR.2019.00718"},{"key":"4963_CR20","doi-asserted-by":"crossref","unstructured":"Liu JB, Huang YP, Zou Q, Wang SC (2019) Learning representative features via constrictive annular loss for image classification. Applied Intelligence, 49(8):3082\u20133092","DOI":"10.1007\/s10489-019-01434-3"},{"key":"4963_CR21","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25:1097\u20131105"},{"key":"4963_CR22","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"4963_CR23","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"4963_CR24","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 conference on computer vision and pattern recognition, pages 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"10","key":"4963_CR25","doi-asserted-by":"publisher","first-page":"3570","DOI":"10.1007\/s10489-019-01468-7","volume":"49","author":"C Yuan","year":"2019","unstructured":"Yuan C, Wu Y, Qin X, Qiao S, Pan Y, Huang P, Liu D, Han N (2019) An effective image classification method for shallow densely connected convolution networks through squeezing and splitting techniques. Applied Intelligence 49(10):3570\u20133586","journal-title":"Applied Intelligence"},{"key":"4963_CR26","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4963_CR27","unstructured":"Park J, Woo S, Lee JY, Kweon IS (2018) Bam: Bottleneck attention module. arXiv:1807.06514"},{"key":"4963_CR28","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV), pages 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"4963_CR29","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.neucom.2020.09.068","volume":"433","author":"H Liu","year":"2021","unstructured":"Liu H, Nie H, Zhang Z, Li YF (2021) Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction. Neurocomputing 433:310\u2013322","journal-title":"Neurocomputing"},{"key":"4963_CR30","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1109\/TMM.2021.3081873","volume":"24","author":"H Liu","year":"2021","unstructured":"Liu H, Fang S, Zhang Z, Li D, Lin K, Wang J (2021) Mfdnet: Collaborative poses perception and matrix fisher distribution for head pose estimation. IEEE Trans Multimedia 24:2449\u20132460","journal-title":"IEEE Trans Multimedia"},{"key":"4963_CR31","doi-asserted-by":"publisher","first-page":"4919","DOI":"10.1109\/TIP.2021.3077136","volume":"30","author":"Y Deng","year":"2021","unstructured":"Deng Y, Chen H, Chen H, Li Y (2021) Learning from images: A distillation learning framework for event cameras. IEEE Trans Image Process 30:4919\u20134931","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"4963_CR32","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2017","unstructured":"Zhou B, Lapedriza A, Khosla A, Oliva A, Torralba A (2017) Places: A 10 million image database for scene recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1452\u20131464","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"4963_CR33","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe DG (2004) Distinctive image features from scale-invariant keypoints. International journal of computer vision 60(2):91\u2013110","journal-title":"International journal of computer vision"},{"key":"4963_CR34","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), volume 1, pages 886\u2013893. Ieee","DOI":"10.1109\/CVPR.2005.177"},{"issue":"3","key":"4963_CR35","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1023\/A:1011139631724","volume":"42","author":"A Oliva","year":"2001","unstructured":"Oliva A, Torralba A (2001) Modeling the shape of the scene: A holistic representation of the spatial envelope. International journal of computer vision 42(3):145\u2013175","journal-title":"International journal of computer vision"},{"issue":"9","key":"4963_CR36","doi-asserted-by":"publisher","first-page":"1704","DOI":"10.1109\/TPAMI.2011.235","volume":"34","author":"H J\u00e9gou","year":"2011","unstructured":"J\u00e9gou H, Perronnin F, Douze M, S\u00e1nchez J, P\u00e9rez P, Schmid C (2011) Aggregating local image descriptors into compact codes. IEEE transactions on pattern analysis and machine intelligence 34(9):1704\u20131716","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"4963_CR37","doi-asserted-by":"crossref","unstructured":"Perronnin F, S\u00e1nchez J, Mensink T (2010) Improving the fisher kernel for large-scale image classification. In European conference on computer vision, pages 143\u2013156. Springer","DOI":"10.1007\/978-3-642-15561-1_11"},{"key":"4963_CR38","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.neucom.2020.06.066","volume":"411","author":"H Liu","year":"2020","unstructured":"Liu H, Wang X, Zhang W, Zhang Z, Li YF (2020) Infrared head pose estimation with multi-scales feature fusion on the irhp database for human attention recognition. Neurocomputing 411:510\u2013520","journal-title":"Neurocomputing"},{"issue":"12","key":"4963_CR39","doi-asserted-by":"publisher","first-page":"8275","DOI":"10.1109\/TCSVT.2021.3073673","volume":"32","author":"Y Deng","year":"2021","unstructured":"Deng Y, Chen H, Li Y (2021) Mvf-net: A multi-view fusion network for event-based object classification. IEEE Transactions on Circuits and Systems for Video Technology 32(12):8275\u20138284","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"4963_CR40","doi-asserted-by":"crossref","unstructured":"Zheng H, Fu J, Mei T, Luo J (2017) Learning multi-attention convolutional neural network for fine-grained image recognition. In Proceedings of the IEEE international conference on computer vision, pages 5209\u20135217","DOI":"10.1109\/ICCV.2017.557"},{"key":"4963_CR41","doi-asserted-by":"crossref","unstructured":"Yang Z, Luo T, Wang D, Hu Z, Gao J, Wang L (2018) Learning to navigate for fine-grained classification. In Proceedings of the European Conference on Computer Vision (ECCV), pages 420\u2013435","DOI":"10.1007\/978-3-030-01264-9_26"},{"issue":"1","key":"4963_CR42","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15(1):1929\u20131958","journal-title":"The journal of machine learning research"},{"key":"4963_CR43","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448\u2013456. PMLR"},{"key":"4963_CR44","doi-asserted-by":"crossref","unstructured":"Singh KK, Lee YJ (2017) Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization. In 2017 IEEE international conference on computer vision (ICCV), pages 3544\u20133553. IEEE","DOI":"10.1109\/ICCV.2017.381"},{"key":"4963_CR45","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 13001\u201313008","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"4963_CR46","unstructured":"DeVries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv:1708.04552"},{"key":"4963_CR47","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv:1506.01497"},{"key":"4963_CR48","doi-asserted-by":"crossref","unstructured":"Quattoni A, Torralba A (2009) Recognizing indoor scenes. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 413\u2013420. IEEE","DOI":"10.1109\/CVPR.2009.5206537"},{"key":"4963_CR49","doi-asserted-by":"crossref","unstructured":"Xiao J, Hays J, Ehinger KA, Oliva A, Torralba A (2010) Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE computer society conference on computer vision and pattern recognition, pages 3485\u20133492. IEEE","DOI":"10.1109\/CVPR.2010.5539970"},{"key":"4963_CR50","unstructured":"Goyal P, Doll\u00e1r P, Girshick R, Noordhuis P, Wesolowski L, Kyrola A, Tulloch A, Jia Y, He K (2017) Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv:1706.02677"},{"key":"4963_CR51","doi-asserted-by":"crossref","unstructured":"Sitaula C, Xiang Y, Aryal S, Lu X (2021) Scene image representation by foreground, background and hybrid features. Expert Systems with Applications, page 115285","DOI":"10.1016\/j.eswa.2021.115285"},{"key":"4963_CR52","doi-asserted-by":"crossref","unstructured":"Guo S, Huang W, Wang L, Qiao Y (2016) Locally supervised deep hybrid model for scene recognition. IEEE transactions on image processing 26(2):808\u2013820","DOI":"10.1109\/TIP.2016.2629443"},{"issue":"6","key":"4963_CR53","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TCSVT.2015.2511543","volume":"27","author":"GS Xie","year":"2015","unstructured":"Xie GS, Zhang XY, Yan S, Liu CL (2015) Hybrid cnn and dictionary-based models for scene recognition and domain adaptation. IEEE Transactions on Circuits and Systems for Video Technology 27(6):1263\u20131274","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"4963_CR54","doi-asserted-by":"crossref","unstructured":"Herranz L, Jiang S, Li X (2016) Scene recognition with cnns: objects, scales and dataset bias. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 571\u2013579","DOI":"10.1109\/CVPR.2016.68"},{"key":"4963_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107256","volume":"102","author":"A L\u00f3pez-Cifuentes","year":"2020","unstructured":"L\u00f3pez-Cifuentes A, Escudero-Vi\u00f1olo M (2020) Jes\u00fas Besc\u00f3s, \u00c1 Garc\u00eda-Mart\u00edn. Semantic-aware scene recognition. Pattern Recognition 102:107256","journal-title":"Semantic-aware scene recognition. Pattern Recognition"},{"key":"4963_CR56","doi-asserted-by":"crossref","unstructured":"Chen G, Song X, Zeng H, Jiang S (2020) Scene recognition with prototype-agnostic scene layout. IEEE Transactions on Image Processing, 29:5877\u20135888","DOI":"10.1109\/TIP.2020.2986599"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04963-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04963-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04963-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T10:16:49Z","timestamp":1730110609000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04963-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,19]]},"references-count":56,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["4963"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04963-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,19]]},"assertion":[{"value":"11 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2023","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}