{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:27:25Z","timestamp":1740122845066,"version":"3.37.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906008"],"award-info":[{"award-number":["61906008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61632006"],"award-info":[{"award-number":["61632006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672066"],"award-info":[{"award-number":["61672066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976011"],"award-info":[{"award-number":["61976011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Common Program of Beijing Municipal Commission of Education","award":["KM202010005013"],"award-info":[{"award-number":["KM202010005013"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s11042-021-10903-2","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T08:05:46Z","timestamp":1619078746000},"page":"25673-25688","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MS-Net: A lightweight separable ConvNet for multi-dimensional image processing"],"prefix":"10.1007","volume":"80","author":[{"given":"Zhenning","family":"Hou","sequence":"first","affiliation":[]},{"given":"Yunhui","family":"Shi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5437-3150","authenticated-orcid":false,"given":"Jin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yingxuan","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,22]]},"reference":[{"key":"10903_CR1","unstructured":"Deep learning in computer vision (2020) Principles and applications"},{"key":"10903_CR2","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Computer vision and pattern recognition, pp 1933\u20131941","DOI":"10.1109\/CVPR.2016.213"},{"issue":"1","key":"10903_CR3","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/T-C.1973.223602","volume":"C-22","author":"M Fischler","year":"1973","unstructured":"Fischler M, Elschlager R (1973) The representation and matching of pictorial structures. IEEE Trans Comput C-22(1):67\u201392","journal-title":"IEEE Trans Comput"},{"key":"10903_CR4","doi-asserted-by":"crossref","unstructured":"Galvez RL, Bandala AA, Dadios EP, Vicerra RRP, Maningo JMZ (2019) Object detection using convolutional neural networks. In: TENCON 2018 - 2018 IEEE Region 10 conference","DOI":"10.1109\/TENCON.2018.8650517"},{"key":"10903_CR5","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"10903_CR6","unstructured":"Goodfellow IJ, Warde-farley D, Mirza M, Courville A, Bengio Y (2013) Maxout networks. In: International conference on machine learning"},{"key":"10903_CR7","unstructured":"Han S, Pool J, Tran J, Dally W (2015) Learning both weights and connections for efficient neural network. In: Advances in neural information processing systems, pp 1135\u20131143"},{"key":"10903_CR8","doi-asserted-by":"crossref","unstructured":"Hassaballah M, Hosny K (2019) recent advances in computer vision: Theories and applications","DOI":"10.1007\/978-3-030-03000-1"},{"key":"10903_CR9","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10903_CR10","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv:1704.04861"},{"key":"10903_CR11","doi-asserted-by":"crossref","unstructured":"Huang G, Liu S, Laurens VDM, Weinberger KQ (2017) Condensenet: An efficient densenet using learned group convolutions","DOI":"10.1109\/CVPR.2018.00291"},{"key":"10903_CR12","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. In: CVPR","DOI":"10.1109\/CVPR.2017.243"},{"key":"10903_CR13","doi-asserted-by":"crossref","unstructured":"Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, Song Y, Guadarrama S et al (2017) Speed\/accuracy trade-offs for modern convolutional object detectors. In: IEEE CVPR","DOI":"10.1109\/CVPR.2017.351"},{"key":"10903_CR14","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <\u20090.5mb model size"},{"issue":"1","key":"10903_CR15","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji S, Xu W, Yang M, Yu K (2013) 3D Convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Machine Intell 35 (1):221\u2013231","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"issue":"8","key":"10903_CR16","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"H Jie","year":"2020","unstructured":"Jie H, Li S, Samuel A, Gang S, Enhua W (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Machine Intell 42(8):2011\u20132023","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"10903_CR17","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":"10903_CR18","doi-asserted-by":"crossref","unstructured":"Li G, Xie Y, Lin L, Yu Y (2017) Instance-level salient object segmentation. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 247\u2013256","DOI":"10.1109\/CVPR.2017.34"},{"key":"10903_CR19","doi-asserted-by":"crossref","unstructured":"Liang M, Hu X (2015) Recurrent convolutional neural network for object recognition. In: Computer vision and pattern recognition, pp 3367\u20133375","DOI":"10.1109\/CVPR.2015.7298958"},{"key":"10903_CR20","unstructured":"Lin M, Chen Q, Yan S (2020) Network in network, arXiv:1312.4400"},{"key":"10903_CR21","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection","DOI":"10.1109\/ICCV.2017.324"},{"key":"10903_CR22","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"10903_CR23","doi-asserted-by":"crossref","unstructured":"Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietik\u00e4inen M (2020) Deep learning for generic object detection: A survey","DOI":"10.1007\/s11263-019-01247-4"},{"key":"10903_CR24","doi-asserted-by":"crossref","unstructured":"Mrazova I, Pihera J, Veleminska J (2013) Can N-dimensional convolutional neural networks distinguish men and women better than humans do?. In: Neural networks (IJCNN), The 2013 international joint conference on. IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2013.6707101"},{"issue":"3","key":"10903_CR25","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s11263-007-0122-4","volume":"79","author":"JC Niebles","year":"2008","unstructured":"Niebles JC, Wang H, Li FF (2008) Unsupervised learning of human action categories using spatial-temporal words. Int J Comput Vis 79(3):299\u2013318","journal-title":"Int J Comput Vis"},{"key":"10903_CR26","doi-asserted-by":"crossref","unstructured":"Qi N, Shi Y, Sun X, Yin B (2016) TenSR: Multi-dimensional tensor sparse representation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5916\u20135925","DOI":"10.1109\/CVPR.2016.637"},{"key":"10903_CR27","doi-asserted-by":"crossref","unstructured":"Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: Imagenet classification using binary convolutional neural networks. In: European conference on computer vision. Springer, pp 525\u2013542","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"10903_CR28","doi-asserted-by":"crossref","unstructured":"Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: An astounding baseline for recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 806\u2013813","DOI":"10.1109\/CVPRW.2014.131"},{"key":"10903_CR29","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":"10903_CR30","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556"},{"key":"10903_CR31","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: Computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10903_CR32","unstructured":"Tan M, Le QV (2019) Efficientnet: Rethinking model scaling for convolutional neural networks"},{"key":"10903_CR33","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":"10903_CR34","unstructured":"Tran A, Guan J, Pilantanakitti T, Cohen P (2014) Action recognition in the frequency domain. In: Acoustics, Speech, and signal processing, IEEE international conference on ICASSP \u201982, pp 1625\u20131628"},{"key":"10903_CR35","doi-asserted-by":"crossref","unstructured":"Yu X, Liu T, Wang X, Tao D (2017) On compressing deep models by low rank and sparse decomposition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7370\u20137379","DOI":"10.1109\/CVPR.2017.15"},{"key":"10903_CR36","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2017) Shufflenet: An extremely efficient convolutional neural network for mobile devices, arXiv:1707.01083","DOI":"10.1109\/CVPR.2018.00716"},{"key":"10903_CR37","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10903-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10903-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10903-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,25]],"date-time":"2022-12-25T04:33:46Z","timestamp":1671942826000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10903-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,22]]},"references-count":37,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10903"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10903-2","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,4,22]]},"assertion":[{"value":"17 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2021","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 they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}