乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T02:52:00Z","timestamp":1706755920496},"reference-count":29,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Computer Vision"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1049\/cvi2.12133","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T06:11:48Z","timestamp":1660111908000},"page":"88-97","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MFNet: Panoptic segmentation network based on multiscale feature weighted fusion and frequency domain attention mechanism"],"prefix":"10.1049","volume":"17","author":[{"given":"Haiwei","family":"Lei","sequence":"first","affiliation":[{"name":"School of Data Science and Technology North University of China Taiyuan China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8460-8184","authenticated-orcid":false,"given":"Fangyuan","family":"He","sequence":"additional","affiliation":[{"name":"School of Data Science and Technology North University of China Taiyuan China"}]},{"given":"Bohui","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Data Science and Technology North University of China Taiyuan China"}]},{"given":"Qian","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Data Science and Technology North University of China Taiyuan China"}]}],"member":"265","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","unstructured":"Kirillov A. et\u00a0al.:Panoptic Segmentation(2018). arXiv preprint arXiv:1801.00868.https:\/\/doi.org\/10.1109\/CVPR.2019.00963","DOI":"10.1109\/CVPR.2019.00963"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"Long J. Shelhamer E. Darrell T.:Fully convolutional networks for semantic segmentation. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.3431\u20133440(2015).https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_2_9_4_1","doi-asserted-by":"crossref","unstructured":"He K. et\u00a0al.:Mask R\u2010CNN. In:Proceedings of the IEEE International Conference on Computer Vision pp.2961\u20132969(2017).https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_2_9_5_1","doi-asserted-by":"crossref","unstructured":"Xiong Y. et\u00a0al.:Upsnet: a unified panoptic segmentation network. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.8818\u20138826(2019).https:\/\/doi.org\/10.1109\/CVPR.2019.00902","DOI":"10.1109\/CVPR.2019.00902"},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","unstructured":"Li Y. et\u00a0al.:Attention\u2010guided unified network for panoptic segmentation. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.7026\u20137035(2019).https:\/\/doi.org\/10.1109\/CVPR.2019.00719","DOI":"10.1109\/CVPR.2019.00719"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"Cheng B. et\u00a0al.:Panoptic\u2010deeplab: a simple strong and fast baseline for\u00a0bottom\u2010up panoptic segmentation. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp.\u00a012475\u201312485(2020).https:\/\/doi.org\/10.1109\/CVPR42600.2020.01249","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2969919"},{"key":"e_1_2_9_9_1","doi-asserted-by":"crossref","unstructured":"Lin T.Y. et\u00a0al.:Feature pyramid networks for object detection. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.2117\u20132125(2017).https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_2_9_10_1","unstructured":"Tao A. Sapra K. Catanzaro B.:Hierarchical multiscale attention for semantic segmentation(2020). arXiv preprint arXiv:2005.10821"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6955"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"Qin Z. et\u00a0al.:FcaNet: frequency channel attention networks(2020). arXiv preprint arXiv:2012.11879","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","unstructured":"Chen Y. et\u00a0al.:BANet: Bidirectional aggregation network with occlusion handling for panoptic segmentation. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp.\u00a03793\u20133802(2020).https:\/\/doi.org\/10.1109\/cvpr42600.2020.00385","DOI":"10.1109\/CVPR42600.2020.00385"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"Wu Y. et\u00a0al.:Bidirectional graph reasoning network for panoptic segmentation. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp.9080\u20139089(2020).https:\/\/doi.org\/10.1109\/CVPR42600.2020.00910","DOI":"10.1109\/CVPR42600.2020.00910"},{"key":"e_1_2_9_15_1","doi-asserted-by":"crossref","unstructured":"Lazarow J. et\u00a0al.:Learning instance occlusion for panoptic segmentation. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp.10720\u201310729(2020).https:\/\/doi.org\/10.1109\/CVPR42600.2020.01073","DOI":"10.1109\/CVPR42600.2020.01073"},{"key":"e_1_2_9_16_1","doi-asserted-by":"crossref","unstructured":"Zhang G. et\u00a0al.:Ada\u2010segment: automated multi\u2010loss adaptation for panoptic segmentation. arXiv preprint arXiv:2012.03603 (2020).https:\/\/doi.org\/10.1093\/adaptation\/apn018","DOI":"10.1093\/adaptation\/apn018"},{"key":"e_1_2_9_17_1","doi-asserted-by":"crossref","unstructured":"Sofiiuk K. Barinova O. Konushin A.:Adaptis: adaptive instance selection network. In:Proceedings of the IEEE\/CVF International Conference on Computer Vision pp.7355\u20137363(2019).https:\/\/doi.org\/10.1109\/ICCV.2019.00745","DOI":"10.1109\/ICCV.2019.00745"},{"key":"e_1_2_9_18_1","unstructured":"Paszke A. et\u00a0al.:Enet: a deep neural network architecture for real\u2010time semantic segmentation. arXiv preprint arXiv:1606.02147 (2020)"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_2_9_20_1","doi-asserted-by":"crossref","unstructured":"Zhao H. et\u00a0al.:Pyramid scene parsing network. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.\u00a02881\u20132890(2017).https:\/\/doi.org\/10.1109\/CVPR.2017.660","DOI":"10.1109\/CVPR.2017.660"},{"key":"e_1_2_9_21_1","doi-asserted-by":"crossref","unstructured":"Ding H. et\u00a0al.:Context contrasted feature and gated multi\u2010scale aggregation for scene segmentation. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.2393\u20132402(2018)","DOI":"10.1109\/CVPR.2018.00254"},{"key":"e_1_2_9_22_1","doi-asserted-by":"crossref","unstructured":"Hu J. Shen L. Sun G.:Squeeze\u2010and\u2010excitation networks. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.7132\u20137141(2018).https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_9_23_1","doi-asserted-by":"crossref","unstructured":"Lee Y. Park J.:Centermask: real\u2010time anchor\u2010free instance segmentation. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp.13906\u201313915(2020).https:\/\/doi.org\/10.1109\/CVPR42600.2020.01392","DOI":"10.1109\/CVPR42600.2020.01392"},{"key":"e_1_2_9_24_1","doi-asserted-by":"crossref","unstructured":"Yu F. Koltun V.:Multiscale context aggregation by dilated convolutions(2015). arXiv preprint arXiv:1511.07122.https:\/\/doi.org\/10.21437\/Interspeech.2019\u20102782","DOI":"10.21437\/Interspeech.2019"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_2_9_26_1","doi-asserted-by":"crossref","unstructured":"Cordts M. et\u00a0al.:The cityscapes dataset for semantic urban scene understanding. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.3213\u20133223(2016).https:\/\/doi.org\/10.1109\/cvpr.2016.350","DOI":"10.1109\/CVPR.2016.350"},{"key":"e_1_2_9_27_1","doi-asserted-by":"crossref","unstructured":"Kirillov A. et\u00a0al.:Panoptic feature pyramid networks. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp.6399\u20136408(2019).https:\/\/doi.org\/10.1109\/cvpr.2019.00656","DOI":"10.1109\/CVPR.2019.00656"},{"key":"e_1_2_9_28_1","unstructured":"DeGeus D. Meletis P. Dubbelman G.:Panoptic segmentation with a joint semantic and instance segmentation network(2018). arXiv preprint arXiv:1809.02110"},{"key":"e_1_2_9_29_1","doi-asserted-by":"crossref","unstructured":"Liu H. et\u00a0al.:An end\u2010to\u2010end network for panoptic segmentation. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp.6172\u20136181(2019).https:\/\/doi.org\/10.1109\/CVPR.2019.00633","DOI":"10.1109\/CVPR.2019.00633"},{"key":"e_1_2_9_30_1","unstructured":"Li J. et\u00a0al.:Learning to fuse things and stuff(2018). arXiv preprint arXiv:1812.01192"}],"container-title":["IET Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/cvi2.12133","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/cvi2.12133","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/cvi2.12133","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,16]],"date-time":"2023-02-16T09:53:48Z","timestamp":1676541228000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1049\/cvi2.12133"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,9]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["10.1049\/cvi2.12133"],"URL":"https:\/\/doi.org\/10.1049\/cvi2.12133","archive":["Portico"],"relation":{},"ISSN":["1751-9632","1751-9640"],"issn-type":[{"value":"1751-9632","type":"print"},{"value":"1751-9640","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,9]]},"assertion":[{"value":"2022-03-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-08-02","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-08-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}