{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:58:05Z","timestamp":1769821085068,"version":"3.49.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T00:00:00Z","timestamp":1599004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T00:00:00Z","timestamp":1599004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2002B02181"],"award-info":[{"award-number":["2002B02181"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["51979085"],"award-info":[{"award-number":["51979085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK2020022539"],"award-info":[{"award-number":["BK2020022539"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Basic Research of Shandong Natural Science Foundation","award":["ZR2019ZD10"],"award-info":[{"award-number":["ZR2019ZD10"]}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Plan of Shandong Province","doi-asserted-by":"crossref","award":["2019GGX101050"],"award-info":[{"award-number":["2019GGX101050"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Major agricultural application technology innovation project of Shandong Province","award":["SD2019NJ007"],"award-info":[{"award-number":["SD2019NJ007"]}]},{"name":"National Key Research and Development Program of China","award":["2018YFB1404102"],"award-info":[{"award-number":["2018YFB1404102"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"name":"New Zealand-China Doctoral Research Scholarships Program"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s00521-020-05313-8","type":"journal-article","created":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T20:02:32Z","timestamp":1599076952000},"page":"5167-5181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Multi-cue based 3D residual network for action recognition"],"prefix":"10.1007","volume":"33","author":[{"given":"Ming","family":"Zong","sequence":"first","affiliation":[]},{"given":"Ruili","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhe","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5420-1463","authenticated-orcid":false,"given":"Maoli","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Johan","family":"Potgieter","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,2]]},"reference":[{"key":"5313_CR1","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.procs.2018.07.059","volume":"133","author":"J Arunnehru","year":"2018","unstructured":"Arunnehru J, Chamundeeswari G, Prasanna Bharathi S (2018) Human action recognition using 3D convolutional neural networks with 3D motion cuboids in surveillance videos. Procedia Computer Sci 133:471\u2013477","journal-title":"Procedia Computer Sci"},{"key":"5313_CR2","doi-asserted-by":"crossref","unstructured":"Baccouche M, Mamalet F, Wolf C, Garcia C, Baskurt A (2011) Sequential deep learning for human action recognition. In: International workshop on human behavior understanding, pp 29\u201339. Springer, Berlin","DOI":"10.1007\/978-3-642-25446-8_4"},{"key":"5313_CR3","doi-asserted-by":"crossref","unstructured":"Brox T, Bruhn A, Papenberg N, Weickert J (2004) High accuracy optical flow estimation based on a theory for warping. In: European conference on computer vision, pp 25\u201336. Springer, Berlin","DOI":"10.1007\/978-3-540-24673-2_3"},{"key":"5313_CR4","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"issue":"7","key":"5313_CR5","doi-asserted-by":"publisher","first-page":"3156","DOI":"10.1109\/TIP.2017.2670143","volume":"26","author":"Chenglizhao Chen","year":"2017","unstructured":"Chen Chenglizhao, Li Shuai, Wang Yongguang, Qin Hong, Hao Aimin (2017) Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion. IEEE Trans Image Process 26(7):3156\u20133170","journal-title":"IEEE Trans Image Process"},{"key":"5313_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.optlastec.2015.12.013","volume":"80","author":"Zhe Chen","year":"2016","unstructured":"Chen Zhe, Wang Xin, Sun Zhen, Wang Zhijian (2016) Motion saliency detection using a temporal Fourier transform. Opt Laser Technol 80:1\u201315","journal-title":"Opt Laser Technol"},{"key":"5313_CR7","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1109\/TCSVT.2018.2870832","volume":"29","author":"R Cong","year":"2018","unstructured":"Cong R, Lei J, Fu H, Cheng MM, Lin W, Huang Q (2018) Review of visual saliency detection with comprehensive information. IEEE Trans Circuits Syst Video Technol 29:2941","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"5313_CR8","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neucom.2011.12.033","volume":"86","author":"X Cui","year":"2012","unstructured":"Cui X, Liu Q, Zhang S, Yang F, Metaxas DN (2012) Temporal spectral residual for fast salient motion detection. Neurocomputing 86:24\u201332","journal-title":"Neurocomputing"},{"key":"5313_CR9","doi-asserted-by":"crossref","unstructured":"Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2625\u20132634","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"5313_CR10","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Wildes RP (2016) Spatiotemporal residual networks for video action recognition. In: Advances in neural information processing systems, pp 3468\u20133476","DOI":"10.1109\/CVPR.2017.787"},{"key":"5313_CR11","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Wildes RP (2017) Spatiotemporal multiplier networks for video action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4768\u20134777","DOI":"10.1109\/CVPR.2017.787"},{"key":"5313_CR12","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1933\u20131941","DOI":"10.1109\/CVPR.2016.213"},{"key":"5313_CR13","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"5313_CR14","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/3075849","author":"W Gong","year":"2018","unstructured":"Gong W, Qi L, Xu Y (2018) Privacy-aware multidimensional mobile service quality prediction and recommendation in distributed fog environment. Wireless Commun Mobile Comput. https:\/\/doi.org\/10.1155\/2018\/3075849","journal-title":"Wireless Commun Mobile Comput"},{"key":"5313_CR15","unstructured":"Guo C, Ma Q, Zhang L (2008) Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: IEEE conference on computer vision and pattern recognition, pp 1\u20138. IEEE"},{"key":"5313_CR16","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y. (2017) Learning spatio-temporal features with 3d residual networks for action recognition. In: Proceedings of the IEEE international conference on computer vision, pp 3154\u20133160","DOI":"10.1109\/ICCVW.2017.373"},{"key":"5313_CR17","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y (2018) Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6546\u20136555","DOI":"10.1109\/CVPR.2018.00685"},{"key":"5313_CR18","doi-asserted-by":"crossref","unstructured":"Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Advances in neural information processing systems, pp 545\u2013552","DOI":"10.7551\/mitpress\/7503.003.0073"},{"key":"5313_CR19","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, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1\u20133","key":"5313_CR20","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"Berthold KP Horn","year":"1981","unstructured":"Horn Berthold KP, Schunck Brian G (1981) Determining optical flow. Artif Intell 17(1\u20133):185\u2013203","journal-title":"Artif Intell"},{"key":"5313_CR21","doi-asserted-by":"crossref","unstructured":"Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: IEEE conference on computer vision and pattern recognition, pp 1\u20138. IEEE","DOI":"10.1109\/CVPR.2007.383267"},{"key":"5313_CR22","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/34.730558","volume":"11","author":"Laurent Itti","year":"1998","unstructured":"Itti Laurent, Koch Christof, Niebur Ernst (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 11:1254\u20131259","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"5313_CR23","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"Shuiwang Ji","year":"2013","unstructured":"Ji Shuiwang, Wei Xu, Yang Ming, Kai Yu (2013) 3d convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221\u2013231","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5313_CR24","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.neucom.2018.09.061","volume":"322","author":"Yuzhu Ji","year":"2018","unstructured":"Ji Yuzhu, Zhang Haijun, Wu QM\u00a0Jonathan (2018) Salient object detection via multi-scale attention cnn. Neurocomputing 322:130\u2013140","journal-title":"Neurocomputing"},{"key":"5313_CR25","doi-asserted-by":"crossref","unstructured":"Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1725\u20131732","DOI":"10.1109\/CVPR.2014.223"},{"key":"5313_CR26","unstructured":"Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Viola F, Green T, Trevor B, Paul N et\u00a0al 2017 The kinetics human action video dataset. arXiv preprint arXiv:1705.06950"},{"key":"5313_CR27","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"5313_CR28","doi-asserted-by":"crossref","unstructured":"Kuehne H, Jhuang H, Garrote E, Poggio T, Serre T (2011) Hmdb: a large video database for human motion recognition. In: 2011 International conference on computer vision, pp 2556\u20132563","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"5313_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05144-7","author":"Z Liu","year":"2020","unstructured":"Liu Z, Li Z, Wang R, Zong M, Ji W (2020) Spatiotemporal saliency-based multi-stream networks with attention-aware LSTM for action recognition. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05144-7","journal-title":"Neural Comput Appl"},{"key":"5313_CR30","doi-asserted-by":"crossref","unstructured":"Liu Z, Li Z, Zong M, Ji W, Wang R, Tian Y (2019) Spatiotemporal saliency based multi-stream networks for action recognition. In: Asian conference on pattern recognition, pp 74\u201384. Springer, Singapore","DOI":"10.1007\/978-981-15-3651-9_8"},{"key":"5313_CR31","unstructured":"Shelhamer E, Long J, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440"},{"issue":"9","key":"5313_CR32","doi-asserted-by":"publisher","first-page":"2425","DOI":"10.1007\/s00521-016-2282-z","volume":"28","author":"Eduardo\u00a0M Pereira","year":"2017","unstructured":"Pereira Eduardo\u00a0M, Ciobanu Lucian, Cardoso Jaime\u00a0S (2017) Cross-layer classification framework for automatic social behavioural analysis in surveillance scenario. Neural Comput Appl 28(9):2425\u20132444","journal-title":"Neural Comput Appl"},{"key":"5313_CR33","doi-asserted-by":"publisher","first-page":"137","DOI":"10.5201\/ipol.2013.26","volume":"2013","author":"JS P\u00e9rez","year":"2013","unstructured":"P\u00e9rez JS, Meinhardt-Llopis E, Facciolo G (2013) TV-L1 optical flow estimation. Image Process On Line 2013:137\u2013150","journal-title":"Image Process On Line"},{"key":"5313_CR34","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1007\/s11280-019-00684-y","volume":"23","author":"L Qi","year":"2019","unstructured":"Qi L, Chen Y, Yuan Y, Fu S, Zhang X, Xu X (2019) A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web 23:1275","journal-title":"World Wide Web"},{"issue":"1","key":"5313_CR35","doi-asserted-by":"publisher","first-page":"155014771668869","DOI":"10.1177\/1550147716688696","volume":"13","author":"Lianyong Qi","year":"2017","unstructured":"Qi Lianyong, Dai Peiqiang, Jiguo Yu, Zhou Zhili, Yanwei Xu (2017) Time-location-frequency-aware internet of things service selection based on historical records. Int J Distrib Sens Netw 13(1):1550147716688696","journal-title":"Int J Distrib Sens Netw"},{"issue":"1\u20132","key":"5313_CR36","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s00607-014-0413-x","volume":"98","author":"Lianyong Qi","year":"2016","unstructured":"Qi Lianyong, Dou Wanchun, Chen Jinjun (2016) Weighted principal component analysis-based service selection method for multimedia services in cloud. Computing 98(1\u20132):195\u2013214","journal-title":"Computing"},{"key":"5313_CR37","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.ins.2018.11.030","volume":"480","author":"Lianyong Qi","year":"2019","unstructured":"Qi Lianyong, Wang Ruili, Chunhua Hu, Li Shancang, He Qiang, Xiaolong Xu (2019) Time-aware distributed service recommendation with privacy-preservation. Inf Sci 480:354\u2013364","journal-title":"Inf Sci"},{"key":"5313_CR38","doi-asserted-by":"publisher","first-page":"4397061","DOI":"10.1155\/2017\/4358536","volume":"2016","author":"L Qi","year":"2016","unstructured":"Qi L, Xu X, Dou W, Yu J, Zhou Z, Zhang X (2016) Time-aware IOE service recommendation on sparse data. Mobile Inf Syst 2016:4397061. https:\/\/doi.org\/10.1155\/2016\/4397061","journal-title":"Mobile Inf Syst"},{"key":"5313_CR39","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/4358536","author":"L Qi","year":"2017","unstructured":"Qi L, Yu J, Zhou Z (2017) An invocation cost optimization method for web services in cloud environment. Scientific Program. https:\/\/doi.org\/10.1155\/2017\/4358536","journal-title":"Scientific Program"},{"key":"5313_CR40","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.future.2018.02.050","volume":"88","author":"Lianyong Qi","year":"2018","unstructured":"Qi Lianyong, Zhang Xuyun, Dou Wanchun, Chunhua Hu, Yang Chi, Chen Jinjun (2018) A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment. Future Gener Comput Syst 88:636\u2013643","journal-title":"Future Gener Comput Syst"},{"issue":"11","key":"5313_CR41","doi-asserted-by":"publisher","first-page":"2616","DOI":"10.1109\/JSAC.2017.2760458","volume":"35","author":"Lianyong Qi","year":"2017","unstructured":"Qi Lianyong, Zhang Xuyun, Dou Wanchun, Ni Qiang (2017) A distributed locality-sensitive hashing-based approach for cloud service recommendation from multi-source data. IEEE J Sel Areas Commun 35(11):2616\u20132624","journal-title":"IEEE J Sel Areas Commun"},{"issue":"9","key":"5313_CR42","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1587\/transinf.2016EDP7490","volume":"100","author":"Lianyong Qi","year":"2017","unstructured":"Qi Lianyong, Zhou Zhili, Jiguo Yu, Liu Qi (2017) Data-sparsity tolerant web service recommendation approach based on improved collaborative filtering. IEICE Trans Inf Syst 100(9):2092\u20132099","journal-title":"IEICE Trans Inf Syst"},{"key":"5313_CR43","doi-asserted-by":"crossref","unstructured":"Qiu Z, Yao T, Mei T (2017) Learning spatio-temporal representation with pseudo-3d residual networks. In: Proceedings of the IEEE international conference on computer vision, pp 5533\u20135541","DOI":"10.1109\/ICCV.2017.590"},{"key":"5313_CR44","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.neucom.2019.07.094","volume":"366","author":"Pourya Shamsolmoali","year":"2019","unstructured":"Shamsolmoali Pourya, Zareapoor Masoumeh, Wang Ruili, Jain Deepak Kumar, Yang Jie (2019) G-ganisr: gradual generative adversarial network for image super resolution. Neurocomputing 366:140\u2013153","journal-title":"Neurocomputing"},{"key":"5313_CR45","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Advances in neural information processing systems, pp 568\u2013576"},{"key":"5313_CR46","unstructured":"Soomro K, Zamir AR, Shah M (2012) Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402"},{"key":"5313_CR47","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.neunet.2019.08.022","volume":"121","author":"Chunwei Tian","year":"2020","unstructured":"Tian Chunwei, Yong Xu, Zuo Wangmeng (2020) Image denoising using deep cnn with batch renormalization. Neural Netw 121:461\u2013473","journal-title":"Neural Netw"},{"key":"5313_CR48","doi-asserted-by":"publisher","unstructured":"Tian C, Xu Y, Zuo W, Zhang B, Fei L, Lin CW (2020) Coarse-to-fine CNN for image super-resolution. IEEE Trans Multimedia.\u00a0https:\/\/doi.org\/10.1109\/TMM.2020.2999182","DOI":"10.1109\/TMM.2020.2999182"},{"key":"5313_CR49","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"key":"5313_CR50","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2018) A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6450\u20136459","DOI":"10.1109\/CVPR.2018.00675"},{"key":"5313_CR51","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1109\/TIP.2018.2890749","volume":"28","author":"Z Tu","year":"2019","unstructured":"Tu Z, Li H, Zhang D, Dauwels J, Li B, Yuan J (2019) Action-stage emphasized spatio-temporal vlad for video action recognition. IEEE Trans Image Process 28:2799","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"5313_CR52","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/TPAMI.2017.2712608","volume":"40","author":"G\u00fcl Varol","year":"2018","unstructured":"Varol G\u00fcl, Laptev Ivan, Schmid Cordelia (2018) Long-term temporal convolutions for action recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1510\u20131517","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5313_CR53","doi-asserted-by":"crossref","unstructured":"Wang H, Kl\u00e4ser A, Schmid C, Liu CL, (2011) Action recognition by dense trajectories. In: IEEE conference on computer vision & pattern recognition, pp 3169\u20133176","DOI":"10.1109\/CVPR.2011.5995407"},{"key":"5313_CR54","doi-asserted-by":"crossref","unstructured":"Wang H, Schmid C (2013) Action recognition with improved trajectories. In: Proceedings of the IEEE international conference on computer vision, pp 3551\u20133558","DOI":"10.1109\/ICCV.2013.441"},{"key":"5313_CR55","doi-asserted-by":"crossref","unstructured":"Wang L, Xiong Y, Wang Z, Qiao Y, Lin D, Tang X, Van Gool L (2016) Temporal segment networks: towards good practices for deep action recognition. In: European conference on computer vision, pp 20\u201336. Springer, Singapore","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"5313_CR56","doi-asserted-by":"crossref","unstructured":"Xue Y, Guo X, Cao X (2012) Motion saliency detection using low-rank and sparse decomposition. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1485\u20131488","DOI":"10.1109\/ICASSP.2012.6288171"},{"key":"5313_CR57","unstructured":"Ng YH Joe, Hausknecht M, Vijayanarasimhan S, Vinyals O, Monga R, Toderici G (2015) Beyond short snippets: deep networks for video classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4694\u20134702"},{"issue":"10","key":"5313_CR58","doi-asserted-by":"publisher","first-page":"2965","DOI":"10.1007\/s00521-017-2900-4","volume":"30","author":"Shaoning Zeng","year":"2018","unstructured":"Zeng Shaoning, Gou Jianping, Yang Xiong (2018) Improving sparsity of coefficients for robust sparse and collaborative representation-based image classification. Neural Comput Appl 30(10):2965\u20132978","journal-title":"Neural Comput Appl"},{"issue":"11","key":"5313_CR59","doi-asserted-by":"publisher","first-page":"7361","DOI":"10.1007\/s00521-018-3579-x","volume":"31","author":"Haijun Zhang","year":"2019","unstructured":"Zhang Haijun, Ji Yuzhu, Huang Wang, Liu Linlin (2019) Sitcom-star-based clothing retrieval for video advertising: a deep learning framework. Neural Comput Appl 31(11):7361\u20137380","journal-title":"Neural Comput Appl"},{"issue":"5","key":"5313_CR60","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","volume":"29","author":"Shichao Zhang","year":"2018","unstructured":"Zhang Shichao, Li Xuelong, Zong Ming, Zhu Xiaofeng, Wang Ruili (2018) Efficient knn classification with different numbers of nearest neighbors. IEEE Trans Neural Netw Learn Syst 29(5):1774\u20131785","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5313_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.04.041","author":"H Zheng","year":"2020","unstructured":"Zheng H, Wang R, Ji W, Zong M, Wong WK, Lai Z, Lv H (2020) Discriminative deep multi-task learning for facial expression recognition. Inf Sci. https:\/\/doi.org\/10.1016\/j.ins.2020.04.041","journal-title":"Inf Sci"},{"key":"5313_CR62","doi-asserted-by":"crossref","unstructured":"Zhou Y, Sun X, Zha ZJ, Zeng W (2018) Mict: Mixed 3d\/2d convolutional tube for human action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 449\u2013458","DOI":"10.1109\/CVPR.2018.00054"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05313-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05313-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05313-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T19:47:01Z","timestamp":1696621621000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05313-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,2]]},"references-count":62,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["5313"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05313-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,2]]},"assertion":[{"value":"19 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}