{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:54:23Z","timestamp":1742997263597,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030880033"},{"type":"electronic","value":"9783030880040"}],"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\/978-3-030-88004-0_23","type":"book-chapter","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:06:25Z","timestamp":1634857585000},"page":"280-292","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Scale-Aware Multi-branch Decoder for Salient Object Detection"],"prefix":"10.1007","author":[{"given":"Yang","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huajun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohua","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhuang","family":"Lai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Kr\u00e4henb\u00fchl, P., Pritch, Y., et al.: Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 733\u2013740. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Yan, Q., Xu, L., Shi, J., et al.: Hierarchical saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155\u20131162 (2013)","DOI":"10.1109\/CVPR.2013.153"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Yang, C., Zhang, L., Lu, H., et al.: Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166\u20133173 (2013)","DOI":"10.1109\/CVPR.2013.407"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, X., Koch, C., et al.: The secrets of salient object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 280\u2013287 (2014)","DOI":"10.1109\/CVPR.2014.43"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Margolin, R., Zelnik-Manor, L., Tal, A.: How to evaluate foreground maps? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2014)","DOI":"10.1109\/CVPR.2014.39"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Zhao, R., Ouyang, W., Li, H., et al.: Saliency detection by multi-context deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265\u20131274 (2015)","DOI":"10.1109\/CVPR.2015.7298731"},{"key":"23_CR7","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)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"23_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Liu, N., Han, J.: Dhsnet: deep hierarchical saliency network for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 678\u2013686 (2016)","DOI":"10.1109\/CVPR.2016.80"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Li, G., Yu, Y.: Deep contrast learning for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 478\u2013487 (2016)","DOI":"10.1109\/CVPR.2016.58"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., et al.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 447\u2013456 (2015)","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"23_CR12","unstructured":"Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5455\u20135463 (2015)"},{"issue":"2","key":"23_CR13","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s41095-019-0149-9","volume":"5","author":"A Borji","year":"2019","unstructured":"Borji, A., Cheng, M.M., Hou, Q., et al.: Salient object detection: a survey. Comput. Vis. Media 5(2), 117\u2013150 (2019)","journal-title":"Comput. Vis. Media"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, H., Wang, Y., et al.: Learning to detect salient objects with image-level supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 136\u2013145 (2017)","DOI":"10.1109\/CVPR.2017.404"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Wang, T., Borji, A., Zhang, L., et al.: A stagewise refinement model for detecting salient objects in images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4019\u20134028 (2017)","DOI":"10.1109\/ICCV.2017.433"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wang, D., Lu, H., et al.: Amulet: aggregating multi-level convolutional features for salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 202\u2013211 (2017)","DOI":"10.1109\/ICCV.2017.31"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, D.: Salient region-based online object tracking. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1170\u20131177. IEEE (2018)","DOI":"10.1109\/WACV.2018.00133"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dai, J., Lu, H., et al.: A bi-directional message passing model for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1741\u20131750 (2018)","DOI":"10.1109\/CVPR.2018.00187"},{"key":"23_CR19","unstructured":"Chen, Z., Zhou, H., Xie, X., et al. Contour loss: Boundary-aware learning for salient object segmentation. arXiv preprint arXiv:1908.01975 (2019)"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., et al.: Basnet: boundary-aware salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7479\u20137489 (2019)","DOI":"10.1109\/CVPR.2019.00766"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3907\u20133916 (2019)","DOI":"10.1109\/CVPR.2019.00403"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, Z., Shen, H., et al.: Centermask: single shot instance segmentation with point representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9313\u20139321 (2020)","DOI":"10.1109\/CVPR42600.2020.00933"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Cheng, M.M., Liu, Y., et al.: Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4548\u20134557 (2017)","DOI":"10.1109\/ICCV.2017.487"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Gong, C., Cao, Y., et al.: Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421 (2018)","DOI":"10.24963\/ijcai.2018\/97"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q.: Stacked cross refinement network for edge-aware salient object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7264\u20137273 (2019)","DOI":"10.1109\/ICCV.2019.00736"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, J.X., Liu, J.J., Fan, D.P., et al.: EGNet: edge guidance network for salient object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8779\u20138788 (2019)","DOI":"10.1109\/ICCV.2019.00887"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Wang, W., Shen, J., Cheng, M.M., et al.: An iterative and cooperative top-down and bottom-up inference network for salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5968\u20135977 (2019)","DOI":"10.1109\/CVPR.2019.00612"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, L., Wang, S., et al.: Detect globally, refine locally: a novel approach to saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3127\u20133135 (2018)","DOI":"10.1109\/CVPR.2018.00330"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Liu, N., Han, J., Yang, M.H.: Picanet: learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3089\u20133098 (2018)","DOI":"10.1109\/CVPR.2018.00326"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Su, J., Li, J., Zhang, Y., et al.: Selectivity or invariance: Boundary-aware salient object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3799\u20133808 (2019)","DOI":"10.1109\/ICCV.2019.00390"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Zhou, H., Xie, X., Lai, J.H., et al.: Interactive two-stream decoder for accurate and fast saliency detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9141\u20139150 (2020)","DOI":"10.1109\/CVPR42600.2020.00916"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Zhang, L., et al.: Multi-scale interactive network for salient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9413\u20139422 (2020)","DOI":"10.1109\/CVPR42600.2020.00943"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88004-0_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T15:07:55Z","timestamp":1709824075000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88004-0_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880033","9783030880040"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88004-0_23","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":"22 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"29 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/2021\/index_en.html","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":"513","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":"201","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":"39% - 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":"5","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":"There were 30 oral and 171 poster presentations at the conference.","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)"}}]}}