{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:15:27Z","timestamp":1740147327699,"version":"3.37.3"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"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":["SIViP"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s11760-022-02278-0","type":"journal-article","created":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T11:02:48Z","timestamp":1659178968000},"page":"705-713","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Grid self-occlusion: a grid self-occlusion data augmentation for better classification"],"prefix":"10.1007","volume":"17","author":[{"given":"Xue","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3710-333X","authenticated-orcid":false,"given":"Hao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boping","family":"Mei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,30]]},"reference":[{"key":"2278_CR1","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"2278_CR2","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2278_CR3","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"2278_CR4","unstructured":"Fu, C.-Y., et al.: Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659 (2017)"},{"key":"2278_CR5","doi-asserted-by":"crossref","unstructured":"Law, H., Deng, J.: Cornernet: detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"2278_CR6","doi-asserted-by":"crossref","unstructured":"Tian, Z., et al.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"issue":"3","key":"2278_CR7","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s11554-020-01054-y","volume":"18","author":"M Meneses","year":"2021","unstructured":"Meneses, M., et al.: SmartSORT an MLP-based method for tracking multiple objects in real-time. J. Real-Time Image Process. 18(3), 913\u2013921 (2021)","journal-title":"J. Real-Time Image Process."},{"issue":"4","key":"2278_CR8","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1007\/s11760-019-01418-3","volume":"13","author":"Y Liang","year":"2019","unstructured":"Liang, Y., et al.: Multiple object tracking by reliable tracklets. Signal Image Video Process. 13(4), 823\u2013831 (2019)","journal-title":"Signal Image Video Process."},{"key":"2278_CR9","unstructured":"Santurkar, S., et al.: How does batch normalization help optimization? Adv. Neural Inf. Process. Syst. 31 (2018)"},{"issue":"1","key":"2278_CR10","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"2278_CR11","unstructured":"Hu, B., et al.: A preliminary study on data augmentation of deep learning for image classification. arXiv preprint arXiv:1906.11887 (2019)"},{"key":"2278_CR12","doi-asserted-by":"crossref","unstructured":"Khosla, C., Saini, B.S.: Enhancing performance of deep learning models with different data augmentation techniques: a survey. In: 2020 International Conference on Intelligent Engineering and Management (ICIEM). IEEE (2020)","DOI":"10.1109\/ICIEM48762.2020.9160048"},{"key":"2278_CR13","doi-asserted-by":"crossref","unstructured":"Grandvalet, Y., Canu, S., Boucheron, S.: Noise injection: theoretical prospects. Neural Comput. 9(5), 1093\u20131108 (1997)","DOI":"10.1162\/neco.1997.9.5.1093"},{"key":"2278_CR14","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., et al.: Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018)","DOI":"10.1109\/CVPR.2019.00020"},{"key":"2278_CR15","doi-asserted-by":"crossref","unstructured":"Gong, C., et al.: KeepAugment: a simple information-preserving data augmentation approach. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00111"},{"key":"2278_CR16","doi-asserted-by":"crossref","unstructured":"Zhong, Z., et al.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, No. 07 (2020)","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"2278_CR17","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"2278_CR18","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Repulsion loss: detecting pedestrians in a crowd. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00811"},{"key":"2278_CR19","doi-asserted-by":"crossref","unstructured":"Singh, K.K., Lee, Y.J.: Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE (2017)","DOI":"10.1109\/ICCV.2017.381"},{"key":"2278_CR20","unstructured":"Gastaldi, X.: Shake-shake regularization. arXiv preprint arXiv:1705.07485 (2017)"},{"key":"2278_CR21","unstructured":"Yamada, Y., Iwamura, M., Kise, K.: Shakedrop regularization (2018)"},{"key":"2278_CR22","doi-asserted-by":"crossref","unstructured":"Shijie, J., et al.: Research on data augmentation for image classification based on convolution neural networks. In: 2017 Chinese Automation Congress (CAC). IEEE (2017)","DOI":"10.1109\/CAC.2017.8243510"},{"key":"2278_CR23","doi-asserted-by":"crossref","unstructured":"Oskam, T., et al.: Fast and stable color balancing for images and augmented reality. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. IEEE (2012)","DOI":"10.1109\/3DIMPVT.2012.36"},{"key":"2278_CR24","unstructured":"Zhang, H., et al.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"2278_CR25","doi-asserted-by":"crossref","unstructured":"Yun, S., et al.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"2278_CR26","unstructured":"Inoue, H.: Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929 (2018)"},{"key":"2278_CR27","doi-asserted-by":"crossref","unstructured":"Tokozume, Y., Ushiku, Y., Harada, T.: Between-class learning for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00575"},{"key":"2278_CR28","unstructured":"Chen, P., et al.: Gridmask data augmentation. arXiv preprint arXiv:2001.04086 (2020)"},{"key":"2278_CR29","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"2278_CR30","doi-asserted-by":"crossref","unstructured":"Duan, K., et al.: Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00667"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02278-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-022-02278-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02278-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T22:24:01Z","timestamp":1679869441000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-022-02278-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,30]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["2278"],"URL":"https:\/\/doi.org\/10.1007\/s11760-022-02278-0","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2022,7,30]]},"assertion":[{"value":"11 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}