{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:54Z","timestamp":1740108054245,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,10,10]],"date-time":"2020-10-10T00:00:00Z","timestamp":1602288000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,10]],"date-time":"2020-10-10T00:00:00Z","timestamp":1602288000000},"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":["31401293"],"award-info":[{"award-number":["31401293"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Special Science and Technology Project of Anhui Province","award":["201903a06020006"],"award-info":[{"award-number":["201903a06020006"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00521-020-05400-w","type":"journal-article","created":{"date-parts":[[2020,10,10]],"date-time":"2020-10-10T18:02:32Z","timestamp":1602352952000},"page":"6389-6403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning region-guided scale-aware feature selection for object detection"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4218-8008","authenticated-orcid":false,"given":"Liu","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rujing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengjun","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,10]]},"reference":[{"key":"5400_CR1","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":"5400_CR2","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556"},{"key":"5400_CR3","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":"5400_CR4","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"5400_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":"5400_CR6","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. arXiv preprint arXiv:180402767"},{"key":"5400_CR7","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. Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"5400_CR8","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"5400_CR9","doi-asserted-by":"crossref","unstructured":"Yang F, Choi W, Lin Y (2016) Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2129\u20132137","DOI":"10.1109\/CVPR.2016.234"},{"key":"5400_CR10","unstructured":"Dai J, Li Y, He K, Sun J (2016) R-FCN: object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379\u2013387"},{"key":"5400_CR11","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"5400_CR12","doi-asserted-by":"crossref","unstructured":"Hu P, Ramanan D (2017) Finding tiny faces. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 951\u2013959","DOI":"10.1109\/CVPR.2017.166"},{"key":"5400_CR13","doi-asserted-by":"crossref","unstructured":"Singh B, Davis LS (2018) An analysis of scale invariance in object detection snip. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3578\u20133587","DOI":"10.1109\/CVPR.2018.00377"},{"issue":"4","key":"5400_CR14","first-page":"985","volume":"20","author":"J Li","year":"2017","unstructured":"Li J, Liang X, Shen S, Xu T, Feng J, Yan S (2017) Scale-aware fast R-CNN for pedestrian detection. IEEE Trans Multimedia 20(4):985\u2013996","journal-title":"IEEE Trans Multimedia"},{"key":"5400_CR15","doi-asserted-by":"crossref","unstructured":"Li Y, Chen Y, Wang N, Zhang Z (2019) Scale-aware trident networks for object detection. In: Proceedings of the IEEE international conference on computer vision, pp 6054\u20136063","DOI":"10.1109\/ICCV.2019.00615"},{"key":"5400_CR16","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"5400_CR17","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"5400_CR18","unstructured":"Fu CY, Liu W, Ranga A, Tyagi A, Berg AC (2017) DSSD: deconvolutional single shot detector. arXiv preprint arXiv:170106659"},{"key":"5400_CR19","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"5400_CR20","doi-asserted-by":"crossref","unstructured":"Zhang S, Wen L, Bian X, Lei Z, Li SZ (2018) Single-shot refinement neural network for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4203\u20134212","DOI":"10.1109\/CVPR.2018.00442"},{"issue":"2","key":"5400_CR21","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","volume":"104","author":"JR Uijlings","year":"2013","unstructured":"Uijlings JR, Van De Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vis 104(2):154\u2013171","journal-title":"Int J Comput Vis"},{"key":"5400_CR22","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91\u201399"},{"key":"5400_CR23","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6154\u20136162","DOI":"10.1109\/CVPR.2018.00644"},{"key":"5400_CR24","doi-asserted-by":"crossref","unstructured":"Pang J, Chen K, Shi J, Feng H, Ouyang W, Lin D (2019) Libra R-CNN: towards balanced learning for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 821\u2013830","DOI":"10.1109\/CVPR.2019.00091"},{"key":"5400_CR25","doi-asserted-by":"crossref","unstructured":"Kong T, Yao A, Chen Y, Sun F (2016) Hypernet: towards accurate region proposal generation and joint object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 845\u2013853","DOI":"10.1109\/CVPR.2016.98"},{"key":"5400_CR26","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"5400_CR27","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:151107122"},{"key":"5400_CR28","doi-asserted-by":"crossref","unstructured":"Zhu C, He Y, Savvides M (2019) Feature selective anchor-free module for single-shot object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 840\u2013849","DOI":"10.1109\/CVPR.2019.00093"},{"key":"5400_CR29","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"3","key":"5400_CR30","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1109\/3477.764879","volume":"29","author":"K Krishna","year":"1999","unstructured":"Krishna K, Murty MN (1999) Genetic k-means algorithm. IEEE Trans Syst Man Cybern Part B (Cybern) 29(3):433\u2013439","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"key":"5400_CR31","doi-asserted-by":"crossref","unstructured":"Neubeck A, Van\u00a0Gool L (2006) Efficient non-maximum suppression. In: 18th international conference on pattern recognition (ICPR\u201906), vol\u00a03. IEEE, pp 850\u2013855","DOI":"10.1109\/ICPR.2006.479"},{"key":"5400_CR32","doi-asserted-by":"crossref","unstructured":"Hecht-Nielsen R (1992) Theory of the backpropagation neural network. In: Neural networks for perception. Elsevier, pp 65\u201393","DOI":"10.1016\/B978-0-12-741252-8.50010-8"},{"key":"5400_CR33","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"5400_CR34","unstructured":"Girshick R, Radosavovic I, Gkioxari G, Doll\u00e1r P, He K (2018) Detectron. https:\/\/github.com\/facebookresearch\/detectron"},{"key":"5400_CR35","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"},{"key":"5400_CR36","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"5400_CR37","doi-asserted-by":"crossref","unstructured":"Zhu X, Hu H, Lin S, Dai J (2019) Deformable convnets v2: more deformable, better results. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9308\u20139316","DOI":"10.1109\/CVPR.2019.00953"},{"issue":"2","key":"5400_CR38","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"key":"5400_CR39","doi-asserted-by":"publisher","first-page":"7722","DOI":"10.1109\/TII.2019.2954956","volume":"16","author":"H Zhang","year":"2019","unstructured":"Zhang H, Li D, Ji Y, Zhou H, Wu W, Liu K (2019) Towards new retail: a benchmark dataset for smart unmanned vending machines. IEEE Trans Ind Inform 16:7722\u20137731","journal-title":"IEEE Trans Ind Inform"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05400-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05400-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05400-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T01:01:05Z","timestamp":1633914065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05400-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,10]]},"references-count":39,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["5400"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05400-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2020,10,10]]},"assertion":[{"value":"8 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}