{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:59:57Z","timestamp":1773953997105,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T00:00:00Z","timestamp":1632441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,9,24]]},"DOI":"10.1145\/3488933.3489001","type":"proceedings-article","created":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T11:36:59Z","timestamp":1645789019000},"page":"380-385","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Tire Pattern Image Retrieval Algorithm Based on Optimized Efficientnet"],"prefix":"10.1145","author":[{"given":"Ying","family":"Liu","sequence":"first","affiliation":[{"name":"Xi'an University of Posts and Telecommunications, China"}]},{"given":"Xin","family":"Che","sequence":"additional","affiliation":[{"name":"Xi'an University of Posts and Telecommunications, China"}]},{"given":"Qiqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Xi'an University of Posts and Telecommunications, China"}]},{"given":"Daxiang","family":"Li","sequence":"additional","affiliation":[{"name":"Xi'an University of Posts and Telecommunications, China"}]},{"given":"Yuliang","family":"Pang","sequence":"additional","affiliation":[{"name":"Xi'an University of Posts and Telecommunications, China"}]},{"given":"Peng","family":"Xiao","sequence":"additional","affiliation":[{"name":"Xi'an University of Posts and Telecommunications, China"}]},{"given":"Sheikh Faisal","family":"Rashid","sequence":"additional","affiliation":[{"name":"Department of Computer Science,University of Engineering and Technology(UET)Lahore,Pakistan, Pakistan"}]}],"member":"320","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","author":"Zhang Xu","year":"2018","unstructured":"Xu Zhang , Wei, and Pan, P. Xiao . 2018.In-Vivo Skin Capacitive Image Classification Using AlexNet Convolution Neural Network . In 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) , June 27-29, 2018 , Chongqing, China, 5 pages. https:\/\/doi.org\/10.1109\/ICIVC. 2018.8492860. Xu Zhang, Wei, and Pan, P. Xiao. 2018.In-Vivo Skin Capacitive Image Classification Using AlexNet Convolution Neural Network. In 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), June 27-29, 2018, Chongqing, China, 5 pages. https:\/\/doi.org\/10.1109\/ICIVC.2018.8492860."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_1_3_1","volume-title":"GPipe: Efficient Training of Giant Neural Networks Using Pipeline Parallelism. In IEEE Conference on Computer Vision and Pattern Recognition","author":"Huang Yanping","year":"2019","unstructured":"Yanping Huang , Youlong Cheng , Ankur Bapna , Orhan Firat , Dehao Chen , Mia Xu Chen , HyoukJoong Lee , Jiquan Ngiam , Quoc V. Le , and Yonghui Wu . 2019 . GPipe: Efficient Training of Giant Neural Networks Using Pipeline Parallelism. In IEEE Conference on Computer Vision and Pattern Recognition , June 27-30, 2016, Las Vegas, NV, USA,9 pages. Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, and Yonghui Wu. 2019. GPipe: Efficient Training of Giant Neural Networks Using Pipeline Parallelism. In IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA,9 pages."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_5_1","volume-title":"British Machine Vision Conference","author":"Nikos","year":"2016","unstructured":"Zagoruyko, S., Nikos K..2016. Wide Residual Networks . In British Machine Vision Conference 2016 .York,UK, https:\/\/doi.org\/10.5244\/C.30.87 Zagoruyko, S., Nikos K..2016.Wide Residual Networks. In British Machine Vision Conference 2016.York,UK, https:\/\/doi.org\/10.5244\/C.30.87"},{"key":"e_1_3_2_1_6_1","first-page":"04861","article-title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","volume":"1704","author":"Andrew","year":"2017","unstructured":"Howard, Andrew G., Menglong Zhu , Bo Chen , Dmitry K., Weijun Wang , Tobias W., Marco A., and Hartwig A.. 2017 . MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications . ArXiv Preprint ArXiv : 1704 . 04861 . Howard, Andrew G., Menglong Zhu, Bo Chen, Dmitry K., Weijun Wang, Tobias W., Marco A., and Hartwig A.. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. ArXiv Preprint ArXiv:1704.04861.","journal-title":"ArXiv Preprint ArXiv"},{"key":"e_1_3_2_1_7_1","volume-title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In International Conference on Machine Learning","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan , and Quoc V. Le . 2019 . EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In International Conference on Machine Learning , July 26-28, 2019 , Rome, Italy , 10 pages. Mingxing Tan, and Quoc V. Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In International Conference on Machine Learning, July 26-28, 2019, Rome, Italy , 10 pages."},{"key":"e_1_3_2_1_8_1","volume-title":"Imagenet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems, 25, 1097-1105","author":"Krizhevsky","year":"2019","unstructured":"Krizhevsky A., Sutskever I., Hinton G. E. 2012. Imagenet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems, 25, 1097-1105 , 2019 . Krizhevsky A., Sutskever I., Hinton G. E. 2012. Imagenet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems, 25, 1097-1105, 2019."},{"key":"e_1_3_2_1_9_1","volume-title":"Transfer Learning Effects on Image Steganalysis with Pre-Trained Deep Residual Neural Network Model. In 2018 IEEE International Conference on Big Data (Big Data),Dec 10-13,2018","year":"2018","unstructured":"Ozcan, S., and Mustacoglu, A .. 2018 . Transfer Learning Effects on Image Steganalysis with Pre-Trained Deep Residual Neural Network Model. In 2018 IEEE International Conference on Big Data (Big Data),Dec 10-13,2018 , Seattle, WA, USA , 8 pages. https:\/\/doi.org\/10.1109\/BigData. 2018 .8622437. Ozcan, S., and Mustacoglu, A.. 2018. Transfer Learning Effects on Image Steganalysis with Pre-Trained Deep Residual Neural Network Model. In 2018 IEEE International Conference on Big Data (Big Data),Dec 10-13,2018, Seattle, WA, USA , 8 pages. https:\/\/doi.org\/10.1109\/BigData.2018.8622437."},{"key":"e_1_3_2_1_10_1","volume-title":"Factorizing and Reconstituting Large-Kernel MBConv for Lightweight Face Recognition. In 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Oct 27-28","author":"Jiang Jing","year":"2019","unstructured":"Yaqi, L., Jing Jiang , Kun Zhang , Yilun Hua , and Miao Cheng . 2019 . Factorizing and Reconstituting Large-Kernel MBConv for Lightweight Face Recognition. In 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Oct 27-28 ,2019, Seoul, Korea (South),9 pages. https:\/\/doi.org\/10.1109\/ICCVW. 2019.00329. Yaqi, L., Jing Jiang, Kun Zhang, Yilun Hua, and Miao Cheng. 2019. Factorizing and Reconstituting Large-Kernel MBConv for Lightweight Face Recognition. In 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Oct 27-28,2019, Seoul, Korea (South),9 pages. https:\/\/doi.org\/10.1109\/ICCVW.2019.00329."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Tingyun H. Yungchang Chang Hsinhung Chou and Chingte C.. 2019. Filter-Based Deep-Compression with Global Average Pooling for Convolutional Networks. Journal of Systems Architecture 95(2019 February) 9\u201318. https:\/\/doi.org\/10.1016\/j.sysarc.2019.02.008.  Tingyun H. Yungchang Chang Hsinhung Chou and Chingte C.. 2019. Filter-Based Deep-Compression with Global Average Pooling for Convolutional Networks. Journal of Systems Architecture 95(2019 February) 9\u201318. https:\/\/doi.org\/10.1016\/j.sysarc.2019.02.008.","DOI":"10.1016\/j.sysarc.2019.02.008"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102422"},{"issue":"2","key":"e_1_3_2_1_13_1","first-page":"022022","article-title":"An overview of overfitting and its solutions. In Journal of Physics: Conference Series","volume":"1168","author":"Xue Ying","year":"2019","unstructured":"Ying Xue . 2019 An overview of overfitting and its solutions. In Journal of Physics: Conference Series . IOP Publishing , 1168 ( 2 ): 022022 . 2019. https:\/\/doi.org\/10.1088\/1742-6596\/1168\/2\/022022. Ying Xue. 2019 An overview of overfitting and its solutions. In Journal of Physics: Conference Series. IOP Publishing, 1168(2): 022022. 2019. https:\/\/doi.org\/10.1088\/1742-6596\/1168\/2\/022022.","journal-title":"IOP Publishing"},{"key":"e_1_3_2_1_14_1","unstructured":"Kangrui Wang and Xiaobing Yu.2021. MobileNet and EfficientNet Demonstration on Google Landmark Recognition Dataset.\u00a0International Core Journal of Engineering(3) https:\/\/doi.org\/10.6919\/ICJE.202103_7(3).0043.  Kangrui Wang and Xiaobing Yu.2021. MobileNet and EfficientNet Demonstration on Google Landmark Recognition Dataset.\u00a0International Core Journal of Engineering(3) https:\/\/doi.org\/10.6919\/ICJE.202103_7(3).0043."},{"key":"e_1_3_2_1_15_1","volume-title":"Asian Conference on Computer Vision. 198-213","author":"Henderson","year":"2016","unstructured":"Henderson P., Ferrari V.. 2016 . End-to-end training of object class detectors for mean average precision . In Asian Conference on Computer Vision. 198-213 , 2016, Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-54193-8_13. Henderson P., Ferrari V.. 2016. End-to-end training of object class detectors for mean average precision. In Asian Conference on Computer Vision. 198-213, 2016, Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-54193-8_13."},{"key":"e_1_3_2_1_16_1","volume-title":"Content-based image retrieval using computational visual attention model. pattern recognition, 48,8,(February","author":"Liu Guanghai","year":"2015","unstructured":"Guanghai Liu , Jingyu Yang , and Zouyong Li .2015. Content-based image retrieval using computational visual attention model. pattern recognition, 48,8,(February 2015 ), 2554-2566. https:\/\/doi.org\/10.1016\/j.patcog.2015.02.005 Guanghai Liu, Jingyu Yang, and Zouyong Li.2015. Content-based image retrieval using computational visual attention model. pattern recognition, 48,8,(February 2015), 2554-2566. https:\/\/doi.org\/10.1016\/j.patcog.2015.02.005"},{"key":"e_1_3_2_1_17_1","article-title":"SIMPLIcity: Semantics-sensitive integrated matching for picture libraries","volume":"2001","author":"Wang J.Z.","year":"2001","unstructured":"J.Z. Wang , Jia Li , and Wiederhold G. , 2001 . SIMPLIcity: Semantics-sensitive integrated matching for picture libraries . IEEE Transactions on pattern analysis and machine intelligence , 2001 , 23,9(2001 September), 947-963. https:\/\/doi.org\/10.1109\/34.955109 J.Z. Wang, Jia Li, and Wiederhold G., 2001.SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on pattern analysis and machine intelligence, 2001, 23,9(2001 September), 947-963. https:\/\/doi.org\/10.1109\/34.955109","journal-title":"IEEE Transactions on pattern analysis and machine intelligence"}],"event":{"name":"AIPR 2021: 2021 4th International Conference on Artificial Intelligence and Pattern Recognition","location":"Xiamen China","acronym":"AIPR 2021"},"container-title":["2021 4th International Conference on Artificial Intelligence and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488933.3489001","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488933.3489001","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:49:00Z","timestamp":1750193340000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488933.3489001"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,24]]},"references-count":17,"alternative-id":["10.1145\/3488933.3489001","10.1145\/3488933"],"URL":"https:\/\/doi.org\/10.1145\/3488933.3489001","relation":{},"subject":[],"published":{"date-parts":[[2021,9,24]]},"assertion":[{"value":"2022-02-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}