{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T02:01:05Z","timestamp":1768096865650,"version":"3.49.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T00:00:00Z","timestamp":1647561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T00:00:00Z","timestamp":1647561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11042-022-12219-1","type":"journal-article","created":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T06:02:54Z","timestamp":1647583374000},"page":"23373-23397","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ADM-Net: attentional-deconvolution module-based net for noise-coupled traffic sign recognition"],"prefix":"10.1007","volume":"81","author":[{"given":"Jun Ho","family":"Chung","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong Won","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tae Koo","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2990-8066","authenticated-orcid":false,"given":"Myo Taeg","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"key":"12219_CR1","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.neunet.2018.01.005","volume":"99","author":"A Arcos-Garcia","year":"2012","unstructured":"Arcos-Garcia A, Alvarez-Garcia JA, Soria-Morillo LM (2012) Deep neural network for traffic sign recognition systems: an analysis of spatial transformers and stochastic optimisation methods. Neural Netw 99:158\u2013165","journal-title":"Neural Netw"},{"key":"12219_CR2","doi-asserted-by":"publisher","first-page":"53330","DOI":"10.1109\/ACCESS.2019.2912311","volume":"7","author":"X Bangquan","year":"2019","unstructured":"Bangquan X, Xiong WX (2019) Real-time embedded traffic sign recognition using efficient convolutional neural network. IEEE Access 7:53330\u201353346","journal-title":"IEEE Access"},{"key":"12219_CR3","doi-asserted-by":"crossref","unstructured":"Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. Neural Netw TricksTrade:437\u2013478","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"12219_CR4","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.neucom.2019.11.068","volume":"377","author":"Q Bi","year":"2020","unstructured":"Bi Q, Qin K, Zhang H, Li Z, Xu K (2020) RADC-Net: a residual attention based convolution network for aerial scene classification. Neurocomputing 377:345\u2013359","journal-title":"Neurocomputing"},{"issue":"2","key":"12219_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-020-3156-7","volume":"64","author":"G Cheng","year":"2021","unstructured":"Cheng G, Li R, Lang C, Han J (2021) Task-wise attention guided part complementary learning for few-shot image classification. Sci China Inf Sci 64(2):1\u201314","journal-title":"Sci China Inf Sci"},{"issue":"5","key":"12219_CR6","doi-asserted-by":"publisher","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","volume":"56","author":"G Cheng","year":"2018","unstructured":"Cheng G, Yang C, Yao X, Guo L, Han J (2018) When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs. IEEE Geosci Remote Sens Lett 56(5):2811\u20132821","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"12219_CR7","doi-asserted-by":"crossref","unstructured":"Chu X, Yang W, Ouyang W, Ma C, Yuille AL, Wang X (2017) Multi-context attention for human pose estimation. 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp 5669\u20135678","DOI":"10.1109\/CVPR.2017.601"},{"key":"12219_CR8","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1007\/s11063-020-10211-0","volume":"51","author":"JH Chung","year":"2020","unstructured":"Chung JH, Kim DW, Kang TK, Lim MT (2020) Traffic sign recognition in harsh environment using attention based convolutional pooling neural network. Neural Process Lett 51:2551\u20132573","journal-title":"Neural Process Lett"},{"key":"12219_CR9","doi-asserted-by":"crossref","unstructured":"Ciresan D, Meier U, Masci J, Schmidhuber J (2011) A committee of neural networks for traffic sign classification. The 2011 International Joint Conference on Neural Networks, pp 1918\u20131921","DOI":"10.1109\/IJCNN.2011.6033458"},{"key":"12219_CR10","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.neunet.2012.02.023","volume":"32","author":"D Ciresan","year":"2012","unstructured":"Ciresan D, Meier U, Masci J, Schmidhuber J (2012) Multi-column deep neural network for traffic sign classification. Neural Netw 32:333\u2013338","journal-title":"Neural Netw"},{"key":"12219_CR11","doi-asserted-by":"crossref","unstructured":"Ding X, Guo Y, Ding G, Han J (2019) ACNet: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks. Proceedings of the IEEE\/CVF, International Conference on Computer Vision (ICCV), pp 1911\u20131920","DOI":"10.1109\/ICCV.2019.00200"},{"issue":"3","key":"12219_CR12","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1109\/TIP.2017.2778563","volume":"27","author":"W Du","year":"2018","unstructured":"Du W, Wang Y, Qiao Y (2018) Recurrent spatial-temporal attention network for action recognition in videos. IEEE Trans Image Process 27(3):1347\u20131360","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"12219_CR13","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","volume":"35","author":"C Farabet","year":"2013","unstructured":"Farabet C, Couprie C, Najman L, LeCun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35 (8):1915\u20131929","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"12219_CR14","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF, Conference on Computer Vision and Pattern Recognition (CVPR), pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"12219_CR15","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. Proc IEEE Conf Comput Vis Pattern Recognit:580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"issue":"2","key":"12219_CR16","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s00521-017-3063-z","volume":"31","author":"A Gudigar","year":"2019","unstructured":"Gudigar A, Chokkadi S, Raghavendra U, Acharya UR (2019) An efficient traffic sign recognition based on graph embedding features. Neural Comput Appl 31(2):395\u2013407","journal-title":"Neural Comput Appl"},{"key":"12219_CR17","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.neucom.2003.09.006","volume":"56","author":"FH Hamker","year":"2004","unstructured":"Hamker FH (2004) Predictions of a model of spatial attention using sum-and max-pooling functions. Neurocomputing 56:329\u2013343","journal-title":"Neurocomputing"},{"key":"12219_CR18","doi-asserted-by":"crossref","unstructured":"Haque WA, Arefin S, Shihavuddin ASM, Hasan MA (2021) DeepThin: a novel lightweight CNN architecture for traffic sign recognition without GPU requirements. Expert Syst Appl 168:114481","DOI":"10.1016\/j.eswa.2020.114481"},{"issue":"5","key":"12219_CR19","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1049\/iet-ipr.2019.0634","volume":"14","author":"A Hechri","year":"2020","unstructured":"Hechri A, Mtibaa A (2020) Two-stage traffic sign detection and recognition based on SVM and convolutional neural networks. IET Image Process 14 (5):939\u2013946","journal-title":"IET Image Process"},{"key":"12219_CR20","doi-asserted-by":"crossref","unstructured":"Hong IP, Hwang YB, Kim DY (2019) Efficient deep learning of image denoising using patch complexity local divide and deep conquer. Pattern Recogn 96:106945","DOI":"10.1016\/j.patcog.2019.06.011"},{"key":"12219_CR21","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3431\u20133440","DOI":"10.1109\/CVPR.2017.243"},{"key":"12219_CR22","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) CCNet: criss-cross attention for semantic segmentation. Proceedings of the IEEE\/CVF, International Conference on Computer Vision (ICCV), pp 603\u2013612","DOI":"10.1109\/ICCV.2019.00069"},{"key":"12219_CR23","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Int Conf Mach Learn:448\u2013456"},{"key":"12219_CR24","unstructured":"Ji Y, Zhang H, Jie Z, Ma L, Wu QMJ (2020) CASNet: a cross-attention siamese network for video salient object detection. IEEE Trans Neural Netw Learn Syst:1\u201315"},{"key":"12219_CR25","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.1109\/TITS.2014.2308281","volume":"15","author":"J Jin","year":"2014","unstructured":"Jin J, Fu K, Zhang C (2014) Traffic sign recognition with hinge loss trained convolutional neural networks. IEEE Trans Intell Trans Syst 15:1991\u20132000","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"12219_CR26","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems. [Online]. Available: http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf, vol 25. Curran Associates, Inc., pp 1097\u20131105"},{"key":"12219_CR27","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2019.02.003","volume":"338","author":"F Lateef","year":"2019","unstructured":"Lateef F, Ruichek Y (2019) Survey on semantic segmentation using deep learning techniques. Neurocomputing 338:321\u2013348","journal-title":"Neurocomputing"},{"key":"12219_CR28","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.patcog.2018.01.015","volume":"79","author":"X Li","year":"2018","unstructured":"Li X, Jie Z, Feng J, Liu C, Yan S (2018) Learning with rethinking: recurrently improving convolutional neural networks through feedback. Pattern Recogn 79:183\u2013194","journal-title":"Pattern Recogn"},{"issue":"1","key":"12219_CR29","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/TITS.2015.2459594","volume":"17","author":"C Liu","year":"2016","unstructured":"Liu C, Chang F, Chen Z, Liu D (2016) Fast traffic sign recognition via high-contrast region extraction and extended sparse representation. IEEE Trans Intell Trans Syst 17(1):79\u201392","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"12219_CR30","doi-asserted-by":"publisher","first-page":"57120","DOI":"10.1109\/ACCESS.2019.2913882","volume":"7","author":"Z Liu","year":"2019","unstructured":"Liu Z, Du J, Tian F, Wen J (2019) MR-CNN: a multi-scale region-based convolutional neural network for small traffic sign recognition. IEEE Access 7:57120\u201357128","journal-title":"IEEE Access"},{"issue":"11","key":"12219_CR31","doi-asserted-by":"publisher","first-page":"5655","DOI":"10.1109\/TNNLS.2017.2787781","volume":"29","author":"J Liu","year":"2018","unstructured":"Liu J, Wang Y, Li Y, Fu J, Li J, Lu H (2018) Collaborative deconvolutional neural networks for joint depth estimation and semantic segmentation. IEEE Trans Neural Netw Learn Syst 29(11):5655\u20135666","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"12219_CR32","doi-asserted-by":"publisher","first-page":"3695","DOI":"10.1109\/TIP.2020.2964518","volume":"29","author":"D Liu","year":"2020","unstructured":"Liu D, Wen B, Jiao J, Liu X, Wang Z, Huang TS (2020) Connecting image denoising and high-level vision tasks via deep learning. IEEE Trans Image Process 29:3695\u20133706","journal-title":"IEEE Trans Image Process"},{"key":"12219_CR33","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"12219_CR34","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Ma C, Shen J, Shao L, Porikli F (2019) See more, know more: unsupervised video object segmentation with co-attention siamese networks. Proceedings of the IEEE\/CVF, Conference on Computer Vision and Pattern Recognition (CVPR), pp 3623\u20133632","DOI":"10.1109\/CVPR.2019.00374"},{"key":"12219_CR35","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Shen J, Crandall DJ, Gool LV (2021) Segmenting objects from relational visual data. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2021.3115815"},{"issue":"4","key":"12219_CR36","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1109\/TITS.2016.2598356","volume":"18","author":"X Lu","year":"2017","unstructured":"Lu X, Wang Y, Zhou X, Zhang Z, Ling Z (2017) Traffic sign recognition via multi-modal tree-structure embedded multi-task learning. IEEE Trans Intell Trans Syst 18(4):960\u2013972","journal-title":"IEEE Trans Intell Trans Syst"},{"issue":"4","key":"12219_CR37","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1109\/TITS.2017.2714691","volume":"19","author":"H Luo","year":"2018","unstructured":"Luo H, Yang Y, Tong B, Wu F, Fan B (2018) Traffic sign recognition using a multi-task convolutional neural network. IEEE Trans Intell Trans Syst 19(4):1100\u20131111","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"12219_CR38","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.compchemeng.2018.07.009","volume":"118","author":"T Mao","year":"2018","unstructured":"Mao T, Zhang Y, Ruan Y, Gao H, Zhou H, Li D (2018) Feature learning and process monitoring of injection molding using convolution-deconvolution auto encoders. Comput Chem Eng 118:77\u201390","journal-title":"Comput Chem Eng"},{"key":"12219_CR39","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.imavis.2019.03.002","volume":"88","author":"A Mhalla","year":"2019","unstructured":"Mhalla A, Chateau T, Amara NEB (2019) Spatio-temporal object detection by deep learning: video-interlacing to improve multi-object tracking. Image Vis Comput 88:120\u2013131","journal-title":"Image Vis Comput"},{"issue":"4","key":"12219_CR40","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TITS.2012.2209421","volume":"13","author":"A Mogelmose","year":"2012","unstructured":"Mogelmose A, Trivedi MM, Moeslund TB (2012) Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans Intell Trans Syst 13(4):1484\u20131497","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"12219_CR41","doi-asserted-by":"crossref","unstructured":"Noh HW, Hong SH, Han BH (2015) Learning deconvolution network for semantic segmentation. Proceedings of the IEEE international conference on computer vision, pp 1520\u20131528","DOI":"10.1109\/ICCV.2015.178"},{"key":"12219_CR42","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.patcog.2016.06.005","volume":"61","author":"N Noord","year":"2017","unstructured":"Noord N, Postma E (2017) Learning scale-variant and scale-invariant features for deep image classification. Pattern Recogn 61:583\u2013592","journal-title":"Pattern Recogn"},{"issue":"5","key":"12219_CR43","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1109\/TNNLS.2017.2676130","volume":"29","author":"Y Pang","year":"2018","unstructured":"Pang Y, Sun M, Jiang X, Li X (2018) Convolution in convolution for network in network. IEEE Trans Neural Netw Learn Syst 29(5):1587\u20131597","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"12219_CR44","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.eswa.2020.113594","volume":"159","author":"SI Saedi","year":"2020","unstructured":"Saedi SI, Khosravi H (2020) A deep neural network approach towards real-time on-branch fruit recognition for precision horticulture. Expert Syst Appl 159:345\u2013359","journal-title":"Expert Syst Appl"},{"key":"12219_CR45","doi-asserted-by":"crossref","unstructured":"Scherer D, Muller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. Int Conf Artif Neural Netw, pp 92\u2013101","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"12219_CR46","doi-asserted-by":"crossref","unstructured":"Sermanet P, LeCun Y (2011) Traffic sign recognition with multi-scale convolutional networks, Neural Networks (IJCNN). The 2011 International Joint Conference on, pp 2809\u20132813","DOI":"10.1109\/IJCNN.2011.6033589"},{"key":"12219_CR47","unstructured":"Sharma S, Kiros R, Salakhutdinov R (2016) Action recognition using visual attention, arXiv:1511.04119"},{"key":"12219_CR48","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.ins.2020.11.026","volume":"569","author":"J Shen","year":"2021","unstructured":"Shen J, Ropbertson N (2021) BBAS: Towards large scale effective ensemble adversarial attacks against deep neural network learning. Inf Sci 569:469\u2013478","journal-title":"Inf Sci"},{"key":"12219_CR49","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556"},{"key":"12219_CR50","unstructured":"Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M (2014) Striving for simplicity: the all convolutional net, arXiv:1412.6806"},{"issue":"1","key":"12219_CR51","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. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"12219_CR52","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.neunet.2012.02.016","volume":"32","author":"J Stallkamp","year":"2012","unstructured":"Stallkamp J, Schlipsing M, Salmen J, lgel C (2012) Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw 32:323\u2013332","journal-title":"Neural Netw"},{"key":"12219_CR53","unstructured":"Stollenga M, Masci J, Gomez F, Schmidhuber J (2014) Design of stabilizing state feedback for delay systems via convex optimization. In: Advances in neural information processing systems, pp 3545\u20133553"},{"key":"12219_CR54","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2016.10.049","volume":"224","author":"M Sun","year":"2017","unstructured":"Sun M, Song Z, Jiang X, Pan J, Pang Y (2017) Learning pooling for convolutional neural network. Neurocomputing 224:96\u2013104","journal-title":"Neurocomputing"},{"key":"12219_CR55","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J (2015) Going deeper with convolutions. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"4","key":"12219_CR56","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/TITS.2019.2913588","volume":"21","author":"D Tabernik","year":"2020","unstructured":"Tabernik D, Skocaj D (2020) Deep learning for large-scale traffic-sign detection and recognition. IEEE Trans Intell Trans Syst 21(4):1427\u20131440","journal-title":"IEEE Trans Intell Trans Syst"},{"issue":"3","key":"12219_CR57","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/s00138-011-0391-3","volume":"25","author":"R Timofte","year":"2021","unstructured":"Timofte R, Zimmermann K, Gool LV (2021) Multi-view traffic sign detection, recognition, and 3D localisation. Mach Vis Appl 25(3):633\u2013647","journal-title":"Mach Vis Appl"},{"key":"12219_CR58","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.neunet.2020.04.015","volume":"127","author":"P Vidnerova","year":"2020","unstructured":"Vidnerova P, Neruda R (2020) Vulnerability of classifiers to evolutionary generated adversarial examples. Neural Netw 127:168\u2013181","journal-title":"Neural Netw"},{"key":"12219_CR59","doi-asserted-by":"crossref","unstructured":"Wang W, Lu X, Shen J, Crandall DJ, Shao L (2019) Zero-shot video object segmentation via attentive graph neural networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp 9236\u20139245","DOI":"10.1109\/ICCV.2019.00933"},{"issue":"11","key":"12219_CR60","doi-asserted-by":"publisher","first-page":"5837","DOI":"10.1109\/TII.2019.2906083","volume":"15","author":"CS Wickramasinghe","year":"2019","unstructured":"Wickramasinghe CS, Amarasinghe K, Manic M (2019) Deep self-organizing maps for unsupervised image classification. IEEE Trans Ind Informat 15 (11):5837\u20135845","journal-title":"IEEE Trans Ind Informat"},{"key":"12219_CR61","doi-asserted-by":"crossref","unstructured":"Wojna Z, Gorban A, Lee DS, Murphy K, Yu Q, Li Y, Ibarz J (2017) Attention-based extraction of structured information from street view imagery, arXiv:1704.03549","DOI":"10.1109\/ICDAR.2017.143"},{"key":"12219_CR62","doi-asserted-by":"publisher","first-page":"59 803","DOI":"10.1109\/ACCESS.2018.2873948","volume":"6","author":"A Wong","year":"2018","unstructured":"Wong A, Shafiee MJ, Jules MS (2018) Micronnet: a highly compact deep convolutional neural network architecture for real-time embedded traffic sign classification. IEEE Access 6:59 803\u201359 810","journal-title":"IEEE Access"},{"issue":"3","key":"12219_CR63","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1049\/iet-its.2017.0066","volume":"12","author":"Z Yan","year":"2018","unstructured":"Yan Z, Feng Y, Cheng C, Fu J, Zhou X, Yuan J (2018) Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy. IET Intell Transp Syst 12(3):186\u2013193","journal-title":"IET Intell Transp Syst"},{"issue":"1","key":"12219_CR64","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/TNNLS.2019.2899936","volume":"31","author":"S Yang","year":"2020","unstructured":"Yang S, Deng B, Wang J, Li H, Lu M, Che Y, Wei X, Loparo KA (2020) Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons. IEEE Trans Neural Netw Learn Syst 31(1):148\u2013162","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"12219_CR65","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1109\/TFUZZ.2018.2856182","volume":"27","author":"S Yang","year":"2019","unstructured":"Yang S, Deng B, Wang J, Liu C, Li H, Lin Q, Fietkiewicz C, Loparo KA (2019) Design of hidden-property-based variable universe fuzzy control for movement disorders and its efficient reconfigurable implementation. IEEE Trans Fuzzy Syst 27(2):304\u2013318","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"12219_CR66","first-page":"97","volume":"15","author":"S Yang","year":"2021","unstructured":"Yang S, Gao T, Wang J, Deng B, Lansdell B, Linares-Barranco B (2021) Efficient spike-driven learning with dendritic event-based processing. Front Neurosci 15:97","journal-title":"Front Neurosci"},{"key":"12219_CR67","doi-asserted-by":"crossref","unstructured":"Yang S, Wang J, Deng B, Azghadi MR, Linares-Barranco B (2021) Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2021.3084250"},{"key":"12219_CR68","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.physa.2017.11.155","volume":"494","author":"S Yang","year":"2018","unstructured":"Yang S, Wei X, Deng B, Liu C, Li H, Wang J (2018) Efficient digital implementation of a conductance-based globus pallidus neuron and the dynamics analysis. Physica A: Stat Mech Appl 494:484\u2013502","journal-title":"Physica A: Stat Mech Appl"},{"issue":"7","key":"12219_CR69","doi-asserted-by":"publisher","first-page":"3423","DOI":"10.1109\/TIP.2019.2896952","volume":"28","author":"Y Yuan","year":"2019","unstructured":"Yuan Y, Xiong Z, Wang Q (2019) VSSA-NET: vertical Spatial sequence attention network for traffic sign detection. IEEE Trans Image Process 28 (7):3423\u20133434","journal-title":"IEEE Trans Image Process"},{"key":"12219_CR70","unstructured":"Zeiler MD, Fergus R (2013) Stochastic pooling for regularization of deep convolutional neural networks, arXiv:1301.3557, pp 2278\u20132324"},{"issue":"7","key":"12219_CR71","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142\u20133155","journal-title":"IEEE Trans Image Process"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12219-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12219-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12219-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T08:39:30Z","timestamp":1655887170000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12219-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":71,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["12219"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12219-1","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,18]]},"assertion":[{"value":"30 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2022","order":4,"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 that there is no conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}