{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:00:41Z","timestamp":1743130841750,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030873608"},{"type":"electronic","value":"9783030873615"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87361-5_1","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T23:54:11Z","timestamp":1632959651000},"page":"3-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Depth-Included Residual Refinement Network for RGB-D Saliency Detection"],"prefix":"10.1007","author":[{"given":"Jinhao","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuqi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuhan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuelong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Boer, P.T.D., Kroese, D., Mannor, S., Rubinstein, R.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134, 19\u201367 (2002)","DOI":"10.1007\/s10479-005-5724-z"},{"issue":"12","key":"1_CR2","doi-asserted-by":"publisher","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","volume":"24","author":"A Borji","year":"2015","unstructured":"Borji, A., Cheng, M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706\u20135722 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"1_CR3","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1109\/TIP.2019.2891104","volume":"28","author":"H Chen","year":"2019","unstructured":"Chen, H., Li, Y.: Three-stream attention-aware network for rgb-d salient object detection. IEEE Trans. Image Process. 28(6), 2825\u20132835 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., Li, Y.: Progressively complementarity-aware fusion network for rgb-d salient object detection, pp. 3051\u20133060 (06 2018)","DOI":"10.1109\/CVPR.2018.00322"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Chen, H., Li, Y., Su, D.: Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for rgb-d salient object detection. Pattern Recogn. 86, 376\u2013385 (2018)","DOI":"10.1016\/j.patcog.2018.08.007"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Chen, S., Fu, Y.: Progressively guided alternate refinement network for RGB-D salient object detection. CoRR abs\/2008.07064 (2020)","DOI":"10.1007\/978-3-030-58598-3_31"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Cong, R., Xu, Q., Huang, Q.: Dpanet: Depth potentiality-aware gated attention network for rgb-d salient object detection. IEEE Trans. Image Process. 1, 7012\u20137024 (2020)","DOI":"10.1109\/TIP.2020.3028289"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Lin, Z., Zhang, Z., Zhu, M., Cheng, M.M.: Rethinking rgb-d salient object detection: models, data sets, and large-scale benchmarks. IEEE Trans. Neural Netw. Learn. Syst. 1, 2075\u20132089 (2020)","DOI":"10.1109\/TNNLS.2020.2996406"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation (2018)","DOI":"10.24963\/ijcai.2018\/97"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Fu, K., Fan, D.P., Ji, G.P., Zhao, Q.: Jl-dcf: Joint learning and densely-cooperative fusion framework for rgb-d salient object detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3049\u20133059 (2020)","DOI":"10.1109\/CVPR42600.2020.00312"},{"key":"1_CR11","unstructured":"Hong, S., You, T., Kwak, S., Han, B.: Online tracking by learning discriminative saliency map with convolutional neural network (2015)"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Ju, R., Ge, L., Geng, W., Ren, T., Wu, G.: Depth saliency based on anisotropic center-surround difference. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 1115\u20131119 (2014)","DOI":"10.1109\/ICIP.2014.7025222"},{"key":"1_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)"},{"key":"1_CR14","doi-asserted-by":"publisher","first-page":"4873","DOI":"10.1109\/TIP.2020.2976689","volume":"29","author":"G Li","year":"2020","unstructured":"Li, G., Liu, Z., Ling, H.: Icnet: Information conversion network for rgb-d based salient object detection. IEEE Trans. Image Process. 29, 4873\u20134884 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Li, G., Zhu, C.: A three-pathway psychobiological framework of salient object detection using stereoscopic technology. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 3008\u20133014 (2017)","DOI":"10.1109\/ICCVW.2017.355"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Li, N., Ye, J., Ji, Y., Ling, H., Yu, J.: Saliency detection on light field. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 2806\u20132813. IEEE Computer Society, USA (2014)","DOI":"10.1109\/CVPR.2014.359"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Liu, J., Hou, Q., Cheng, M., Feng, J., Jiang, J.: A simple pooling-based design for real-time salient object detection. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3912\u20133921 (2019)","DOI":"10.1109\/CVPR.2019.00404"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Liu, N., Zhang, N., Han, J.: Learning selective self-mutual attention for rgb-d saliency detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13753\u201313762 (2020)","DOI":"10.1109\/CVPR42600.2020.01377"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"M\u00e1ttyus, G., Luo, W., Urtasun, R.: Deeproadmapper: extracting road topology from aerial images. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3458\u20133466 (2017)","DOI":"10.1109\/ICCV.2017.372"},{"key":"1_CR20","unstructured":"Niu, Y., Geng, Y., Li, X., Liu, F.: Leveraging stereopsis for saliency analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 454\u2013461 (2012)"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhang, L., Zhao, X., Lu, H.: Hierarchical dynamic filtering network for rgb-d salient object detection (2020)","DOI":"10.1007\/978-3-030-58595-2_15"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Piao, Y., Ji, W., Li, J., Zhang, M., Lu, H.: Depth-induced multi-scale recurrent attention network for saliency detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 7253\u20137262 (2019)","DOI":"10.1109\/ICCV.2019.00735"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Piao, Y., Rong, Z., Zhang, M., Ren, W., Lu, H.: A2dele: adaptive and attentive depth distiller for efficient rgb-d salient object detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9057\u20139066 (2020)","DOI":"10.1109\/CVPR42600.2020.00908"},{"key":"1_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (09 2014)"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Su, J., Li, J., Zhang, Y., Xia, C., Tian, Y.: Selectivity or invariance: boundary-aware salient object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3798\u20133807 (2019)","DOI":"10.1109\/ICCV.2019.00390"},{"issue":"1","key":"1_CR26","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TPAMI.2017.2662005","volume":"40","author":"W Wang","year":"2018","unstructured":"Wang, W., Shen, J., Yang, R., Porikli, F.: Saliency-aware video object segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 20\u201333 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR27","unstructured":"Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: 2003 The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, vol. 2, pp. 1398\u20131402 (2003)"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Zhuge, Y., Lu, H., Zhang, L., Qian, M., Yu, Y.: Multi-source weak supervision for saliency detection. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6067\u20136076 (2019)","DOI":"10.1109\/CVPR.2019.00623"},{"issue":"4","key":"1_CR29","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1109\/TGRS.2014.2357078","volume":"53","author":"F Zhang","year":"2015","unstructured":"Zhang, F., Du, B., Zhang, L.: Saliency-guided unsupervised feature learning for scene classification. IEEE Trans. Geosci. Remote Sens. 53(4), 2175\u20132184 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Uc-net: Uncertainty inspired rgb-d saliency detection via conditional variational autoencoders. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8579\u20138588 (2020)","DOI":"10.1109\/CVPR42600.2020.00861"},{"key":"1_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, M., Ren, W., Piao, Y., Rong, Z., Lu, H.: Select, supplement and focus for rgb-d saliency detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3469\u20133478 (2020)","DOI":"10.1109\/CVPR42600.2020.00353"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Zhao, J., Cao, Y., Fan, D., Cheng, M., Li, X., Zhang, L.: Contrast prior and fluid pyramid integration for rgbd salient object detection. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3922\u20133931 (2019)","DOI":"10.1109\/CVPR.2019.00405"},{"key":"1_CR33","doi-asserted-by":"crossref","unstructured":"Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3586\u20133593 (2013)","DOI":"10.1109\/CVPR.2013.460"},{"key":"1_CR34","doi-asserted-by":"crossref","unstructured":"Zhao, T., Wu, X.: Pyramid feature attention network for saliency detection (2019)","DOI":"10.1109\/CVPR.2019.00320"},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Zhao, X., Pang, Y., Zhang, L., Lu, H., Zhang, L.: Suppress and balance: a simple gated network for salient object detection (2020)","DOI":"10.1007\/978-3-030-58536-5_3"},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, L., Pang, Y., Lu, H., Zhang, L.: A single stream network for robust and real-time rgb-d salient object detection (2020)","DOI":"10.1007\/978-3-030-58542-6_39"},{"key":"1_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, C., Cai, X., Huang, K., Li, T.H., Li, G.: Pdnet: prior-model guided depth-enhanced network for salient object detection. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 199\u2013204 (2019)","DOI":"10.1109\/ICME.2019.00042"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87361-5_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T23:56:01Z","timestamp":1632959761000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87361-5_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030873608","9783030873615"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87361-5_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haikou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2021.csig.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"421","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"198","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was postponed due to the COVID19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}