{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:39:45Z","timestamp":1740145185536,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11554-024-01509-6","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T02:01:45Z","timestamp":1721008905000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A resource-efficient partial 3D convolution for gesture recognition"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9855-6223","authenticated-orcid":false,"given":"Gongzheng","family":"Chen","sequence":"first","affiliation":[]},{"given":"Zhenghong","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Jue","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jijian","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"key":"1509_CR1","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"1509_CR2","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Adam, H.: Searching for mobilenetv3. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1509_CR3","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1509_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: 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 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1509_CR5","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H. T., & Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European Conference on Computer Vision (ECCV). pp 116\u2013131 (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"issue":"6","key":"1509_CR6","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"key":"1509_CR7","unstructured":"Chen, S., Xie, E., Ge, C., Chen, R., Liang, D., Luo, P.: Cyclemlp: a mlp-like architecture for dense prediction. arXiv preprint arXiv:2107.10224 (2021)"},{"key":"1509_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1509_CR9","doi-asserted-by":"crossref","unstructured":"Kopuklu, O., Kose, N., Gunduz, A., Rigoll, G.: Resource efficient 3d convolutional neural networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops. (2019)","DOI":"10.1109\/ICCVW.2019.00240"},{"key":"1509_CR10","doi-asserted-by":"crossref","unstructured":"Xiee, S., Girshick, R., Doll\u00e1r, P., Tu, Z., & He, K.: Aggregated residual transformations for deep neural networks. In\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"1509_CR11","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"8","key":"1509_CR12","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1007\/s11517-022-02583-3","volume":"60","author":"P Shan","year":"2022","unstructured":"Shan, P., Fu, C., Dai, L., Jia, T., Tie, M., Liu, J.: Automatic skin lesion classification using a new densely connected convolutional network with an SF module. Med. Biol. Eng. Comput. 60(8), 2173\u20132188 (2022)","journal-title":"Med. Biol. Eng. Comput."},{"key":"1509_CR13","doi-asserted-by":"crossref","unstructured":"Maaz, M., Shaker, A., Cholakkal, H., Khan, S., Zamir, S.W., Anwer, R.M., Shahbaz Khan, F.: Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications. In European Conference on Computer Vision (pp. 3\u201320). Cham: Springer Nature Switzerland (2022)","DOI":"10.1007\/978-3-031-25082-8_1"},{"key":"1509_CR14","unstructured":"Mehta, S., Rastegari, M.: Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv 2021. arXiv preprint arXiv:2110.02178"},{"key":"1509_CR15","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D. P., Song, K., Liang, D., Shao, L.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 568\u2013578 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"1509_CR16","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: more features from cheap operations. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 1580\u20131589 (2020)","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"1509_CR17","unstructured":"Tan, M., & Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. pp. 6105\u20136114. PMLR (2019)"},{"key":"1509_CR18","unstructured":"Tan, M., Le, Q.: Efficientnetv2: smaller models and faster training. In International Conference on Machine Learning. pp. 10096\u201310106. PMLR (2021)"},{"key":"1509_CR19","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: a lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)"},{"key":"1509_CR20","unstructured":"Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., Kaiser, \u0141.: Universal transformers. arXiv preprint arXiv:1807.03819 (2018)"},{"key":"1509_CR21","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision. pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"1509_CR22","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: 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 (2017)","DOI":"10.1109\/CVPR.2017.502"},{"key":"1509_CR23","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Yao, T., Mei, T.: Learning spatio-temporal representation with pseudo-3d residual networks. In Proceedings of the IEEE International Conference on Computer Vision. pp. 5533\u20135541 (2017)","DOI":"10.1109\/ICCV.2017.590"},{"key":"1509_CR24","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6450\u20136459 (2018)","DOI":"10.1109\/CVPR.2018.00675"},{"key":"1509_CR25","doi-asserted-by":"crossref","unstructured":"Xie, S., Sun, C., Huang, J., Tu, Z., Murphy, K.: Rethinking spatiotemporal feature learning: speed-accuracy trade-offs in video classification. In Proceedings of the European Conference on Computer Vision (ECCV). pp. 305\u2013321 (2018)","DOI":"10.1007\/978-3-030-01267-0_19"},{"key":"1509_CR26","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 6202\u20136211 (2019)","DOI":"10.1109\/ICCV.2019.00630"},{"issue":"2","key":"1509_CR27","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1007\/s40747-022-00858-8","volume":"9","author":"G Chen","year":"2023","unstructured":"Chen, G., Dong, Z., Wang, J., Xia, L.: Parallel temporal feature selection based on improved attention mechanism for dynamic gesture recognition. Complex Intell. Syst. 9(2), 1377\u20131390 (2023)","journal-title":"Complex Intell. Syst."},{"key":"1509_CR28","doi-asserted-by":"crossref","unstructured":"Lin, J., Gan, C., Han, S.: Tsm: temporal shift module for efficient video understanding. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 7083\u20137093 (2019)","DOI":"10.1109\/ICCV.2019.00718"},{"issue":"3","key":"1509_CR29","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1145\/3007787.3001163","volume":"44","author":"S Han","year":"2016","unstructured":"Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M.A., Dally, W.J.: EIE: efficient inference engine on compressed deep neural network. ACM SIGARCH Comput. Archit. News 44(3), 243\u2013254 (2016)","journal-title":"ACM SIGARCH Comput. Archit. News"},{"key":"1509_CR30","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In Proceedings of the IEEE International Conference on Computer Vision. pp. 2736\u20132744 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"1509_CR31","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. ar**v preprint ar**v:1612.03928. (2016)"},{"key":"1509_CR32","doi-asserted-by":"crossref","unstructured":"Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J.: Quantized convolutional neural networks for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4820\u20134828 (2016)","DOI":"10.1109\/CVPR.2016.521"},{"key":"1509_CR33","doi-asserted-by":"crossref","unstructured":"Ding, X., Guo, Y., Ding, G., Han, J.: Acnet: strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 1911\u20131920 (2019)","DOI":"10.1109\/ICCV.2019.00200"},{"key":"1509_CR34","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: Making vgg-style convnets great again. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 13733\u201313742 (2021)","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"1509_CR35","doi-asserted-by":"crossref","unstructured":"Vasu, P.K.A., Gabriel, J., Zhu, J., Tuzel, O., Ranjan, A.: MobileOne: an improved one millisecond mobile backbone. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 7907\u20137917\/ (2023)","DOI":"10.1109\/CVPR52729.2023.00764"},{"key":"1509_CR36","doi-asserted-by":"crossref","unstructured":"Materzynska, J., Berger, G., Bax, I., Memisevic, R.: The jester dataset: a large-scale video dataset of human gestures. In Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00349"},{"issue":"5","key":"1509_CR37","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1109\/TMM.2018.2808769","volume":"20","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Cao, C., Cheng, J., Lu, H.: EgoGesture: a new dataset and benchmark for egocentric hand gesture recognition. IEEE Trans. Multimed. 20(5), 1038\u20131050 (2018)","journal-title":"IEEE Trans. Multimed."},{"key":"1509_CR38","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., Kautz, J.: Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4207\u20134215. (2016)","DOI":"10.1109\/CVPR.2016.456"},{"issue":"2","key":"1509_CR39","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TBIOM.2020.2968216","volume":"2","author":"O K\u00f6p\u00fckl\u00fc","year":"2020","unstructured":"K\u00f6p\u00fckl\u00fc, O., Gunduz, A., Kose, N., Rigoll, G.: Online dynamic hand gesture recognition including efficiency analysis. IEEE Trans. Biom. Behav. Identity Sci. 2(2), 85\u201397 (2020)","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"1509_CR40","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision pp. 618\u2013626. (2017)","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01509-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01509-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01509-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T16:32:42Z","timestamp":1724776362000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01509-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,15]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1509"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01509-6","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"type":"print","value":"1861-8200"},{"type":"electronic","value":"1861-8219"}],"subject":[],"published":{"date-parts":[[2024,7,15]]},"assertion":[{"value":"14 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"In this article author Gongzheng Chen\u2019s Orchid Id has been added.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"132"}}