{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:16:06Z","timestamp":1775578566298,"version":"3.50.1"},"reference-count":52,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":89,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51975347"],"award-info":[{"award-number":["51975347"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51907117"],"award-info":[{"award-number":["51907117"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["18030501300"],"award-info":[{"award-number":["18030501300"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>This article proposes an innovative RGBD saliency model, that is, attention\u2010guided feature integration network, which can extract and fuse features and perform saliency inference. Specifically, the model first extracts multimodal and level deep features. Then, a series of attention modules are deployed to the multilevel RGB and depth features, yielding enhanced deep features. Next, the enhanced multimodal deep features are hierarchically fused. Lastly, the RGB and depth boundary features, that is, low\u2010level spatial details, are added to the integrated feature to perform saliency inference. The key points of the AFI\u2010Net are the attention\u2010guided feature enhancement and the boundary\u2010aware saliency inference, where the attention module indicates salient objects coarsely, and the boundary information is used to equip the deep feature with more spatial details. Therefore, salient objects are well characterized, that is, well highlighted. The comprehensive experiments on five challenging public RGBD datasets clearly exhibit the superiority and effectiveness of the proposed AFI\u2010Net.<\/jats:p>","DOI":"10.1155\/2021\/8861446","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T22:05:10Z","timestamp":1617228310000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["AFI\u2010Net:\u2009Attention\u2010Guided Feature Integration Network for RGBD Saliency Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7780-1883","authenticated-orcid":false,"given":"Liming","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6359-4091","authenticated-orcid":false,"given":"Shuguang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Chai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shubin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5481-4494","authenticated-orcid":false,"given":"Xingjie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5896-4502","authenticated-orcid":false,"given":"Zhaomin","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"RallisI. GeorgoulasI. DoulamisN. VoulodimosA. andTerzopoulosP. Extraction of key postures from 3D human motion data for choreography summarization Proceedings of the 2017 9th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games) September 2017 Athens Greece IEEE 94\u2013101.","DOI":"10.1109\/VS-GAMES.2017.8056576"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2755566"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"SongS. LichtenbergS. P. XiaoJ. andSunR.-D. A rgb-d scene understanding benchmark suite Proceedings of the Computer Vision and Pattern Recognition CVPR June 2015 Boston MA USA IEEE 567\u2013576.","DOI":"10.1109\/CVPR.2015.7298655"},{"key":"e_1_2_9_4_2","doi-asserted-by":"crossref","unstructured":"QiX. LiaoR. JiaJ. FidlerS. andUrtasunR. 3d graph neural networks for rgbd semantic segmentation Proceedings of the Computer Vision and Pattern Recognition CVPR July 2017 Honolulu HI USA IEEE 5199\u20135208.","DOI":"10.1109\/ICCV.2017.556"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2014.2305100"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-015-2512-x"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"CaneT.andFerrymanJ. Saliency-based detection for maritime object tracking Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops July 2016 Las Vegas NV USA 18\u201325.","DOI":"10.1109\/CVPRW.2016.159"},{"key":"e_1_2_9_8_2","first-page":"583","article-title":"Maritime targets detection from ground cameras exploiting semi-supervised machine learning","author":"Protopapadakis E.","year":"2015","journal-title":"VISAPP"},{"key":"e_1_2_9_9_2","unstructured":"WonW. J. LeeM. andSonJ. W. Implementation of road traffic signs detection based on saliency map model Proceedings of the 2008 IEEE Intelligent Vehicles Symposium June 2008 Eindhoven The Netherlands IEEE 542\u2013547."},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2016.2535402"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"MarchesottiL. CifarelliC. andCsurkaG. A framework for visual saliency detection with applications to image thumbnailing Proceedings of the 2009 IEEE 12th International Conference on Computer Vision October 2009 Kyoto Japan IEEE 2232\u20132239.","DOI":"10.1109\/ICCV.2009.5459467"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.12.023"},{"key":"e_1_2_9_13_2","unstructured":"NiuY. GengY. LiX. andLiuF. Leveraging stereopsis for saliency analysis Proceedings of the Computer Vision and Pattern Recognition CVPR June 2012 Providence RI USA IEEE 454\u2013461."},{"key":"e_1_2_9_14_2","doi-asserted-by":"crossref","unstructured":"ChengY. FuH. WeiX. XiaoJ. andCaoX. Depth enhanced saliency detection method Proceedings of the International Conference on Internet Multimedia Computing and Service ICIMCS July 2014 Xiamen China ACM 23\u201327.","DOI":"10.1145\/2632856.2632866"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"PengH. LiB. XiongW. HuW. andJiR. RGBD salient object detection: a benchmark and algorithms Proceedings of the European Conference on Computer Vision ECCV September 2014 Zurich Switzerland Springer 92\u2013109.","DOI":"10.1007\/978-3-319-10578-9_7"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"JuR. GeL. GengW. RenT. andWuG. Depth saliency based on anisotropic center-surround difference Proceedings of the International Conference on Image Processing ICIP October 2014 Paris France IEEE 1115\u20131119.","DOI":"10.1109\/ICIP.2014.7025222"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2015.07.002"},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"FengD. BarnesN. YouS. andMcCarthyC. Local background enclosure for RGB-D salient object detection Proceedings of the Computer Vision and Pattern Recognition CVPR July 2016 Las Vegas NV USA IEEE 2343\u20132350.","DOI":"10.1109\/CVPR.2016.257"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/lsp.2016.2557347"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","unstructured":"GuoJ. RenT. andBeiJ. Salient object detection for RGB-D image via saliency evolution Proceedings of the International Conference on Multimedia and Expo ICME July 2016 Seattle WA USA IEEE 1\u20136.","DOI":"10.1109\/ICME.2016.7552907"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2017.2711277"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/lsp.2017.2688136"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"ZhuC. LiG. WangW. andWangR. An innovative salient object detection using center-dark channel prior Proceedings of the International Conference on Computer Vision ICCV October 2017 Venice Italy IEEE 1509\u20131515.","DOI":"10.1109\/ICCVW.2017.178"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2017.2682981"},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","unstructured":"ShigematsuR. FengD. YouS. andBarnesN. Learning RGB-D Salient Object Detection using background enclosure depth contrast and top-down features Proceedings of the International Conference on Computer Vision ICCV October 2017 Venice Italy IEEE 2749\u20132757.","DOI":"10.1109\/ICCVW.2017.323"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.07.026"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2761775"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"ChenH.andLiY. Progressively complementarity-aware fusion network for RGB-D salient object detection Proceedings of the Computer Vision and Pattern Recognition CVPR June 2018 Salt Lake City UT USA IEEE 3051\u20133060.","DOI":"10.1109\/CVPR.2018.00322"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.07.012"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2934986"},{"key":"e_1_2_9_31_2","doi-asserted-by":"crossref","unstructured":"ZhuC. CaiX. HuangK. LiT. H. andLiG. Pdnet: prior-model guided depth-enhanced network for salient object detection Proceedings of the International Conference on Multimedia and Expo ICME July 2019 Shanghai China IEEE 199\u2013204.","DOI":"10.1109\/ICME.2019.00042"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2913107"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.08.007"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2019.2891104"},{"key":"e_1_2_9_35_2","doi-asserted-by":"crossref","unstructured":"ZhaoJ. X. CaoY. FanD. P. ChengM. M. LiX. Y. andZhangL. Contrast prior and fluid pyramid integration for RGBD salient object detection Proceedings of the Computer Vision and Pattern Recognition CVPR June 2019 California CA USA IEEE 3927\u20133936.","DOI":"10.1109\/CVPR.2019.00405"},{"key":"e_1_2_9_36_2","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani A.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2019.05.018"},{"key":"e_1_2_9_38_2","doi-asserted-by":"crossref","unstructured":"WangT. ZhangL. WangS.et al. Detect globally refine locally: a novel approach to saliency detection Proceedings of the Computer Vision and Pattern Recognition CVPR June 2018 Salt Lake City UT USA IEEE 3127\u20133135.","DOI":"10.1109\/CVPR.2018.00330"},{"key":"e_1_2_9_39_2","doi-asserted-by":"crossref","unstructured":"ZhangX.andTiantian WangJ. Q. H. L. G. W. Progressive attention guided recurrent network for salient object detection Proceedings of the Computer Vision and Pattern Recognition CVPR June 2018 Salt Lake City UT USA IEEE 714\u2013722.","DOI":"10.1109\/CVPR.2018.00081"},{"key":"e_1_2_9_40_2","doi-asserted-by":"crossref","unstructured":"LuoZ. MishraA. AchkarA. EichelJ. LiS. andJodoinP. M. Non-local deep features for salient object detection Proceedings of the Computer Vision and Pattern Recognition CVPR July 2017 Honolulu HI USA IEEE 6609\u20136617.","DOI":"10.1109\/CVPR.2017.698"},{"key":"e_1_2_9_41_2","doi-asserted-by":"crossref","unstructured":"LiuJ. J. HouQ. ChengM. M. FengJ. andJiangJ. A simple pooling-based design for real-time salient object detection Proceedings of the Computer Vision and Pattern Recognition CVPR June 2019 California CA USA IEEE 3917\u20133926.","DOI":"10.1109\/CVPR.2019.00404"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/34.730558"},{"key":"e_1_2_9_43_2","article-title":"Rethinking RGB-D salient object detection: models, data sets, and large-scale benchmarks","author":"Fan D. P.","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_2_9_44_2","first-page":"1","article-title":"RGB-D salient object detection: a survey","volume":"7","author":"Zhou T.","year":"2020","journal-title":"Computational Visual Media"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10175806"},{"key":"e_1_2_9_46_2","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan K.","year":"2014","journal-title":"International Conference on Learning Representations"},{"key":"e_1_2_9_47_2","doi-asserted-by":"crossref","unstructured":"GuptaS. GirshickR. Arbel\u00e1ezP. andMalikJ. Learning rich features from RGB-D images for object detection and segmentation Proceedings of the European Conference on Computer Vision ECCV September 2014 Zurich Switzerland Springer 345\u2013360 https:\/\/doi.org\/10.1007\/978-3-319-10584-0_23 2-s2.0-84906344142.","DOI":"10.1007\/978-3-319-10584-0_23"},{"key":"e_1_2_9_48_2","doi-asserted-by":"crossref","unstructured":"JiaY. ShelhamerE. DonahueJ.et al. Caffe: convolutional architecture for fast feature embedding Proceedings of the ACM International Conference on Multimedia MM June 2014 California CA USA ACM 675\u2013678.","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_2_9_49_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Delving deep into rectifiers: surpassing human-level performance on imagenet classification Proceedings of the International Conference on Computer Vision ICCV December 2015 Chile CL USA IEEE 1026\u20131034.","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_2_9_50_2","doi-asserted-by":"crossref","unstructured":"LiN. YeJ. JiY. LingH. andYuJ. Saliency detection on light field Proceedings of the Computer Vision and Pattern Recognition CVPR June 2014 Columbus OH USA IEEE 2806\u20132813.","DOI":"10.1109\/CVPR.2014.359"},{"key":"e_1_2_9_51_2","doi-asserted-by":"crossref","unstructured":"FanD. P. ChengM. M. LiuY. LiT. andBorjiA. Structure-measure: a new way to evaluate foreground maps Proceedings of the International Conference on Computer Vision ICCV October 2017 Venice Italy IEEE 4548\u20134557.","DOI":"10.1109\/ICCV.2017.487"},{"key":"e_1_2_9_52_2","doi-asserted-by":"crossref","unstructured":"FanD. P. GongC. CaoY. RenB. ChengM. M. andBorjiA. Enhanced-alignment measure for binary foreground map evaluation Proceedings of the 27th International Joint Conference on Artificial Intelligence. IJCAI July 2018 Stockholm Sweden 698\u2013704.","DOI":"10.24963\/ijcai.2018\/97"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/8861446.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/8861446.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/8861446","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T12:04:57Z","timestamp":1722945897000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/8861446"}},"subtitle":[],"editor":[{"given":"Anastasios D.","family":"Doulamis","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/8861446"],"URL":"https:\/\/doi.org\/10.1155\/2021\/8861446","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2020-09-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8861446"}}