乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:10:39Z","timestamp":1740143439368,"version":"3.37.3"},"reference-count":73,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61836011"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61822208, and 61632019"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"crossref","award":["2018497"],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"As an important topic in the multimedia and computer vision fields, salient object detection has been researched for years. Recently, state-of-the-art performance has been witnessed with the aid of the fully convolutional networks (FCNs) and the various pyramid-like encoder-decoder frameworks. Starting from a common encoder-decoder architecture, we enhance a residual refinement network with feature purification for better saliency estimation. To this end, we improve the global knowledge streams with intermediate supervisions for global saliency estimation and design a specific feature subtraction module for residual learning, respectively. On the basis of the strengthened network, we also introduce an attribute encoding sub-network (AENet) with a grid aggregation block (GAB) to guide the final saliency predictor to obtain more accurate saliency maps. Furthermore, the network is trained with a novel constraint loss besides the traditional cross-entropy loss to yield the finer results. Extensive experiments on five public benchmarks show our method achieves better or comparable performance compared with previous state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3440694","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T14:44:29Z","timestamp":1626965069000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Residual Refinement Network with Attribute Guidance for Precise Saliency Detection"],"prefix":"10.1145","volume":"17","author":[{"given":"Feng","family":"Lin","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Wengang","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Jiajun","family":"Deng","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}]},{"given":"Yan","family":"Lu","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}]},{"given":"Houqiang","family":"Li","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2021,7,22]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1109\/TIP.2015.2487833"},{"doi-asserted-by":"publisher","key":"e_1_2_1_2_1","DOI":"10.5555\/2354409.2354899"},{"key":"e_1_2_1_3_1","volume-title":"GCNet: Non-local networks meet squeeze-excitation networks and beyond. arXiv preprint arXiv:1904.11492","author":"Cao Yue","year":"2019","unstructured":"Yue Cao , Jiarui Xu , Stephen Lin , Fangyun Wei , and Han Hu. 2019. GCNet: Non-local networks meet squeeze-excitation networks and beyond. arXiv preprint arXiv:1904.11492 ( 2019 ). Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, and Han Hu. 2019. GCNet: Non-local networks meet squeeze-excitation networks and beyond. arXiv preprint arXiv:1904.11492 (2019)."},{"doi-asserted-by":"publisher","key":"e_1_2_1_4_1","DOI":"10.1007\/978-3-030-01240-3_15"},{"doi-asserted-by":"publisher","key":"e_1_2_1_5_1","DOI":"10.1109\/TPAMI.2014.2345401"},{"doi-asserted-by":"publisher","key":"e_1_2_1_6_1","DOI":"10.1145\/3177745"},{"doi-asserted-by":"publisher","key":"e_1_2_1_7_1","DOI":"10.5555\/3304415.3304513"},{"doi-asserted-by":"publisher","key":"e_1_2_1_8_1","DOI":"10.1109\/CVPR.2011.5995445"},{"doi-asserted-by":"publisher","key":"e_1_2_1_9_1","DOI":"10.1007\/s11263-014-0733-5"},{"doi-asserted-by":"publisher","key":"e_1_2_1_10_1","DOI":"10.1109\/CVPR.2019.00172"},{"doi-asserted-by":"publisher","key":"e_1_2_1_11_1","DOI":"10.5244\/C.31.181"},{"doi-asserted-by":"publisher","key":"e_1_2_1_12_1","DOI":"10.1109\/TMM.2018.2859746"},{"doi-asserted-by":"publisher","key":"e_1_2_1_13_1","DOI":"10.1109\/TIP.2012.2199502"},{"doi-asserted-by":"publisher","key":"e_1_2_1_14_1","DOI":"10.1145\/2632267"},{"doi-asserted-by":"publisher","key":"e_1_2_1_15_1","DOI":"10.5555\/2354409.2354755"},{"key":"e_1_2_1_16_1","volume-title":"Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision. 2961\u20132969","author":"He Kaiming","year":"2017","unstructured":"Kaiming He , Georgia Gkioxari , Piotr Doll\u00e1r , and Ross Girshick . 2017 . Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision. 2961\u20132969 . Kaiming He, Georgia Gkioxari, Piotr Doll\u00e1r, and Ross Girshick. 2017. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision. 2961\u20132969."},{"doi-asserted-by":"publisher","key":"e_1_2_1_17_1","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"publisher","key":"e_1_2_1_18_1","DOI":"10.5555\/3045118.3045183"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3203\u20133212","author":"Hou Qibin","unstructured":"Qibin Hou , Ming-Ming Cheng , Xiaowei Hu , Ali Borji , Zhuowen Tu , and Philip H. S. Torr . 2017. Deeply supervised salient object detection with short connections . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3203\u20133212 . Qibin Hou, Ming-Ming Cheng, Xiaowei Hu, Ali Borji, Zhuowen Tu, and Philip H. S. Torr. 2017. Deeply supervised salient object detection with short connections. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3203\u20133212.","key":"e_1_2_1_19_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_20_1","DOI":"10.1109\/CVPR.2018.00745"},{"doi-asserted-by":"publisher","key":"e_1_2_1_21_1","DOI":"10.1109\/34.730558"},{"doi-asserted-by":"publisher","key":"e_1_2_1_22_1","DOI":"10.1109\/ICCV.2013.209"},{"doi-asserted-by":"publisher","key":"e_1_2_1_23_1","DOI":"10.1109\/CVPR.2013.271"},{"doi-asserted-by":"publisher","key":"e_1_2_1_24_1","DOI":"10.1109\/CVPR.2014.118"},{"doi-asserted-by":"publisher","key":"e_1_2_1_25_1","DOI":"10.1109\/ICCV.2011.6126499"},{"doi-asserted-by":"publisher","key":"e_1_2_1_26_1","DOI":"10.5555\/2999134.2999257"},{"doi-asserted-by":"publisher","key":"e_1_2_1_27_1","DOI":"10.1109\/CVPR.2016.399"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5455\u20135463","author":"Li Guanbin","year":"2015","unstructured":"Guanbin Li and Yizhou Yu . 2015 . Visual saliency based on multiscale deep features . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5455\u20135463 . Guanbin Li and Yizhou Yu. 2015. Visual saliency based on multiscale deep features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5455\u20135463."},{"doi-asserted-by":"publisher","key":"e_1_2_1_29_1","DOI":"10.1109\/TIP.2016.2602079"},{"doi-asserted-by":"publisher","key":"e_1_2_1_30_1","DOI":"10.1109\/CVPR.2014.43"},{"key":"e_1_2_1_31_1","volume-title":"Deep reasoning with multi-scale context for salient object detection. arXiv preprint arXiv:1901.08362","author":"Li Zun","year":"2019","unstructured":"Zun Li , Congyan Lang , Yunpeng Chen , Junhao Liew , and Jiashi Feng . 2019. Deep reasoning with multi-scale context for salient object detection. arXiv preprint arXiv:1901.08362 ( 2019 ). Zun Li, Congyan Lang, Yunpeng Chen, Junhao Liew, and Jiashi Feng. 2019. Deep reasoning with multi-scale context for salient object detection. arXiv preprint arXiv:1901.08362 (2019)."},{"doi-asserted-by":"publisher","key":"e_1_2_1_32_1","DOI":"10.1109\/ICCV.2017.324"},{"doi-asserted-by":"publisher","key":"e_1_2_1_33_1","DOI":"10.1109\/CVPR.2019.00404"},{"doi-asserted-by":"publisher","key":"e_1_2_1_34_1","DOI":"10.1109\/CVPR.2016.80"},{"doi-asserted-by":"publisher","key":"e_1_2_1_35_1","DOI":"10.1109\/CVPR.2018.00326"},{"doi-asserted-by":"publisher","key":"e_1_2_1_36_1","DOI":"10.1109\/TPAMI.2010.70"},{"key":"e_1_2_1_37_1","volume-title":"Salient object detection via high-to-low hierarchical context aggregation. arXiv preprint arXiv:1812.10956","author":"Liu Yun","year":"2018","unstructured":"Yun Liu , Yu Qiu , Le Zhang , JiaWang Bian , Guang-Yu Nie , and Ming-Ming Cheng . 2018. Salient object detection via high-to-low hierarchical context aggregation. arXiv preprint arXiv:1812.10956 ( 2018 ). Yun Liu, Yu Qiu, Le Zhang, JiaWang Bian, Guang-Yu Nie, and Ming-Ming Cheng. 2018. Salient object detection via high-to-low hierarchical context aggregation. arXiv preprint arXiv:1812.10956 (2018)."},{"doi-asserted-by":"publisher","key":"e_1_2_1_38_1","DOI":"10.1109\/ICCV.2019.00132"},{"doi-asserted-by":"publisher","key":"e_1_2_1_39_1","DOI":"10.1109\/CVPR.2015.7298965"},{"doi-asserted-by":"publisher","key":"e_1_2_1_40_1","DOI":"10.1109\/CVPR.2017.698"},{"doi-asserted-by":"publisher","key":"e_1_2_1_41_1","DOI":"10.1145\/2996463"},{"doi-asserted-by":"publisher","key":"e_1_2_1_42_1","DOI":"10.1109\/CVPR.2019.00766"},{"doi-asserted-by":"publisher","key":"e_1_2_1_43_1","DOI":"10.1109\/TMM.2017.2763780"},{"doi-asserted-by":"publisher","key":"e_1_2_1_44_1","DOI":"10.5555\/2354409.2355062"},{"doi-asserted-by":"publisher","key":"e_1_2_1_45_1","DOI":"10.5555\/2354409.2354676"},{"doi-asserted-by":"publisher","key":"e_1_2_1_46_1","DOI":"10.1109\/TPAMI.2015.2465960"},{"doi-asserted-by":"publisher","key":"e_1_2_1_47_1","DOI":"10.1109\/CVPR.2016.89"},{"key":"e_1_2_1_48_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"doi-asserted-by":"publisher","key":"e_1_2_1_49_1","DOI":"10.1109\/ICCV.2019.00390"},{"doi-asserted-by":"publisher","key":"e_1_2_1_50_1","DOI":"10.1109\/CVPR.2015.7298938"},{"doi-asserted-by":"publisher","key":"e_1_2_1_51_1","DOI":"10.1109\/CVPR.2017.404"},{"doi-asserted-by":"publisher","key":"e_1_2_1_52_1","DOI":"10.1007\/978-3-319-46493-0_50"},{"doi-asserted-by":"publisher","key":"e_1_2_1_53_1","DOI":"10.1109\/TPAMI.2018.2846598"},{"doi-asserted-by":"publisher","key":"e_1_2_1_54_1","DOI":"10.1109\/CVPR.2018.00330"},{"doi-asserted-by":"publisher","key":"e_1_2_1_55_1","DOI":"10.1109\/CVPR.2019.00154"},{"doi-asserted-by":"publisher","key":"e_1_2_1_56_1","DOI":"10.1109\/CVPR.2019.00834"},{"doi-asserted-by":"publisher","key":"e_1_2_1_57_1","DOI":"10.1109\/CVPR.2019.00403"},{"doi-asserted-by":"publisher","key":"e_1_2_1_58_1","DOI":"10.1109\/ICCV.2019.00736"},{"doi-asserted-by":"publisher","key":"e_1_2_1_59_1","DOI":"10.1109\/TMM.2018.2830098"},{"doi-asserted-by":"publisher","key":"e_1_2_1_60_1","DOI":"10.1109\/TIP.2012.2216276"},{"doi-asserted-by":"publisher","key":"e_1_2_1_61_1","DOI":"10.5555\/3045118.3045336"},{"doi-asserted-by":"publisher","key":"e_1_2_1_62_1","DOI":"10.1109\/CVPR.2013.407"},{"doi-asserted-by":"publisher","key":"e_1_2_1_63_1","DOI":"10.1109\/TMM.2017.2693022"},{"key":"e_1_2_1_64_1","volume-title":"Proceedings of the IEEE International Conference on Computer Vision. 3788\u20133797","author":"Yingyue Xu","year":"2019","unstructured":"Xu Yingyue , Xu Dan , Hong Xiaopeng , Ouyang Wanli , Ji Rongrong , Xu Min , and Zhao Guoying . 2019 . Structured modeling of joint deep feature and prediction refinement for salient object detection . In Proceedings of the IEEE International Conference on Computer Vision. 3788\u20133797 . Xu Yingyue, Xu Dan, Hong Xiaopeng, Ouyang Wanli, Ji Rongrong, Xu Min, and Zhao Guoying. 2019. Structured modeling of joint deep feature and prediction refinement for salient object detection. In Proceedings of the IEEE International Conference on Computer Vision. 3788\u20133797."},{"doi-asserted-by":"publisher","key":"e_1_2_1_65_1","DOI":"10.1145\/3107956"},{"doi-asserted-by":"publisher","key":"e_1_2_1_66_1","DOI":"10.1109\/CVPR.2018.00187"},{"doi-asserted-by":"publisher","key":"e_1_2_1_67_1","DOI":"10.1109\/ICCV.2017.31"},{"doi-asserted-by":"publisher","key":"e_1_2_1_68_1","DOI":"10.1109\/ICCV.2017.32"},{"doi-asserted-by":"publisher","key":"e_1_2_1_69_1","DOI":"10.1109\/CVPR.2018.00081"},{"doi-asserted-by":"publisher","key":"e_1_2_1_70_1","DOI":"10.1109\/ICCV.2019.00887"},{"doi-asserted-by":"publisher","key":"e_1_2_1_71_1","DOI":"10.1109\/CVPR.2019.00320"},{"doi-asserted-by":"publisher","key":"e_1_2_1_72_1","DOI":"10.1109\/TMM.2018.2845667"},{"doi-asserted-by":"publisher","key":"e_1_2_1_73_1","DOI":"10.1609\/aaai.v33i01.33019340"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3440694","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T01:58:59Z","timestamp":1672624739000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3440694"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,22]]},"references-count":73,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,8,31]]}},"alternative-id":["10.1145\/3440694"],"URL":"https:\/\/doi.org\/10.1145\/3440694","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"type":"print","value":"1551-6857"},{"type":"electronic","value":"1551-6865"}],"subject":[],"published":{"date-parts":[[2021,7,22]]},"assertion":[{"value":"2020-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}