{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:39:25Z","timestamp":1774946365819,"version":"3.50.1"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030695248","type":"print"},{"value":"9783030695255","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69525-5_42","type":"book-chapter","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T16:21:16Z","timestamp":1614356476000},"page":"706-722","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Local Context Attention for Salient Object Segmentation"],"prefix":"10.1007","author":[{"given":"Jing","family":"Tan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyi","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuntao","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuwen","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"42_CR1","doi-asserted-by":"crossref","unstructured":"Liu, J., Hou, Q., Cheng, M.M., Feng, J., Jiang, J.: A simple pooling-based design for real-time salient object detection. In: CVPR, pp. 3917\u20133926 (2019)","DOI":"10.1109\/CVPR.2019.00404"},{"key":"42_CR2","doi-asserted-by":"crossref","unstructured":"Feng, M., Lu, H., Ding, E.: Attentive feedback network for boundary-aware salient object detection. In: CVPR, pp. 1623\u20131632 (2019)","DOI":"10.1109\/CVPR.2019.00172"},{"key":"42_CR3","doi-asserted-by":"crossref","unstructured":"Wu, R., Feng, M., Guan, W., Wang, D., Lu, H., Ding, E.: A mutual learning method for salient object detection with intertwined multi-supervision. In: CVPR, pp. 8150\u20138159 (2019)","DOI":"10.1109\/CVPR.2019.00834"},{"key":"42_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wang, D., Lu, H., Wang, H., Ruan, X.: Amulet: aggregating multi-level convolutional features for salient object detection. In: ICCV, pp. 202\u2013211 (2017)","DOI":"10.1109\/ICCV.2017.31"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Li, G., Yu, Y.: Deep contrast learning for salient object detection. In: CVPR, pp. 478\u2013487 (2016)","DOI":"10.1109\/CVPR.2016.58"},{"key":"42_CR6","doi-asserted-by":"crossref","unstructured":"Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.H.S.: Deeply supervised salient object detection with short connections. In: CVPR, pp. 5300\u20135309 (2017)","DOI":"10.1109\/CVPR.2017.563"},{"key":"42_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, Q., Zhang, D., Han, J.: Employing deep part-object relationships for salient object detection. In: ICCV, pp. 1232\u20131241 (2019)","DOI":"10.1109\/ICCV.2019.00132"},{"key":"42_CR8","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhao, S., Shen, J., Hoi, S.C.H., Borji, A.: Salient object detection with pyramid attention and salient edges. In: CVPR, pp. 1448\u20131457 (2019)","DOI":"10.1109\/CVPR.2019.00154"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Zhang, P., Zhang, J., Lin, Z.L., Lu, H.: Towards high-resolution salient object detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00733"},{"key":"42_CR10","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Zhuge, Y.Z., Lu, H., Zhang, L., Qian, M., Yu, Y.: Multi-source weak supervision for saliency detection. In: CVPR, pp. 6074\u20136083 (2019)","DOI":"10.1109\/CVPR.2019.00623"},{"key":"42_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhang, J., Lin, Z., Lu, H., He, Y.: CapSal: leveraging captioning to boost semantics for salient object detection. In: CVPR, pp. 6024\u20136033 (2019)","DOI":"10.1109\/CVPR.2019.00618"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Zhao, J., Liu, J., Fan, D.P., Cao, Y., Yang, J., Cheng, M.M.: EGNet: edge guidance network for salient object detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00887"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR, pp. 1155\u20131162 (2013)","DOI":"10.1109\/CVPR.2013.153"},{"key":"42_CR14","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Learning to detect salient objects with image-level supervision. In: CVPR, pp. 3796\u20133805 (2017)","DOI":"10.1109\/CVPR.2017.404"},{"key":"42_CR15","doi-asserted-by":"crossref","unstructured":"Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: CVPR, pp. 3166\u20133173 (2013)","DOI":"10.1109\/CVPR.2013.407"},{"key":"42_CR16","unstructured":"Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: CVPR, pp. 5455\u20135463 (2015)"},{"key":"42_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: CVPR, pp. 280\u2013287 (2014)","DOI":"10.1109\/CVPR.2014.43"},{"key":"42_CR18","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"42_CR19","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R.B., Gupta, A., He, K.: Non-local neural networks. In: CVPR, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"42_CR20","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: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00735"},{"key":"42_CR21","unstructured":"Yuan, Y., Wang, J.: OCNet: object context network for scene parsing (2019)"},{"key":"42_CR22","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, L., Lu, H., Zhang, P., Ruan, X.: Saliency detection with recurrent fully convolutional networks. In: ECCV, pp. 825\u2013841 (2016)","DOI":"10.1007\/978-3-319-46493-0_50"},{"key":"42_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dai, J., Lu, H., He, Y., Wang, G.: A bi-directional message passing model for salient object detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00187"},{"key":"42_CR24","doi-asserted-by":"crossref","unstructured":"Lee, G., Tai, Y.W., Kim, J.: Deep saliency with encoded low level distance map and high level features. In: CVPR, pp. 660\u2013668 (2016)","DOI":"10.1109\/CVPR.2016.78"},{"key":"42_CR25","doi-asserted-by":"crossref","unstructured":"Tang, Y., Wu, X., Bu, W.: Deeply-supervised recurrent convolutional neural network for saliency detection. In: ACM Multimedia, pp. 397\u2013401 (2016)","DOI":"10.1145\/2964284.2967250"},{"key":"42_CR26","unstructured":"Zhang, J., Dai, Y., Porikli, F., He, M.: Deep edge-aware saliency detection (2017)"},{"key":"42_CR27","doi-asserted-by":"crossref","unstructured":"Li, X., Yang, F., Cheng, H., Liu, W., Shen, D.: Contour knowledge transfer for salient object detection. In: ECCV, pp. 370\u2013385 (2018)","DOI":"10.1007\/978-3-030-01267-0_22"},{"key":"42_CR28","doi-asserted-by":"crossref","unstructured":"Yang, J., Price, B.L., Cohen, S., Lee, H., Yang, M.H.: Object contour detection with a fully convolutional encoder-decoder network. In: CVPR, pp. 193\u2013202 (2016)","DOI":"10.1109\/CVPR.2016.28"},{"key":"42_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR, pp. 6230\u20136239 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"42_CR30","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV, pp. 833\u2013851 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"42_CR31","unstructured":"Li, H., Xiong, P., An, J., Wang, L.: Pyramid attention network for semantic segmentation. In: BMVC (2018)"},{"key":"42_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Context encoding for semantic segmentation. In: CVPR, pp. 7151\u20137160 (2018)","DOI":"10.1109\/CVPR.2018.00747"},{"key":"42_CR33","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. In: CVPR, pp. 3640\u20133649 (2016)","DOI":"10.1109\/CVPR.2016.396"},{"issue":"3","key":"42_CR34","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2015","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583\u2013596 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"42_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1007\/978-3-642-33765-9_50","volume-title":"Computer Vision \u2013 ECCV 2012","author":"JF Henriques","year":"2012","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 702\u2013715. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33765-9_50"},{"key":"42_CR36","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: CVPR, pp. 2544\u20132550 (2010)","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"42_CR37","doi-asserted-by":"crossref","unstructured":"Luo, Z., Mishra, A.K., Achkar, A., Eichel, J.A., Li, S., Jodoin, P.M.: Non-local deep features for salient object detection. In: CVPR, pp. 6593\u20136601 (2017)","DOI":"10.1109\/CVPR.2017.698"},{"key":"42_CR38","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"key":"42_CR39","doi-asserted-by":"crossref","unstructured":"Zhao, T., Wu, X.: Pyramid feature attention network for saliency detection. In: CVPR, pp. 3085\u20133094 (2019)","DOI":"10.1109\/CVPR.2019.00320"},{"key":"42_CR40","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., Girshick, R.B.: Training region-based object detectors with online hard example mining. In: CVPR, pp. 761\u2013769 (2016)","DOI":"10.1109\/CVPR.2016.89"},{"key":"42_CR41","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)"},{"key":"42_CR42","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: CVPR, pp. 3907\u20133916 (2019)","DOI":"10.1109\/CVPR.2019.00403"},{"key":"42_CR43","doi-asserted-by":"crossref","unstructured":"Wang, T., et al.: Detect globally, refine locally: a novel approach to saliency detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00330"},{"key":"42_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, T., Qi, J., Lu, H., Wang, G.: Progressive attention guided recurrent network for salient object detection. In: CVPR, pp. 714\u2013722 (2018)","DOI":"10.1109\/CVPR.2018.00081"},{"key":"42_CR45","doi-asserted-by":"crossref","unstructured":"Wang, T., Borji, A., Zhang, L., Zhang, P., Lu, H.: A stagewise refinement model for detecting salient objects in images. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.433"},{"key":"42_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wang, D., Lu, H., Wang, H., Yin, B.: Learning uncertain convolutional features for accurate saliency detection. In: ICCV, pp. 212\u2013221 (2017)","DOI":"10.1109\/ICCV.2017.32"},{"key":"42_CR47","doi-asserted-by":"crossref","unstructured":"Liu, N., Han, J.: Deep hierarchical saliency network for salient object detection. In: CVPR, pp. 678\u2013686 (2016)","DOI":"10.1109\/CVPR.2016.80"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69525-5_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T17:09:33Z","timestamp":1614359373000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69525-5_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030695248","9783030695255"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69525-5_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","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":"254","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":"33% - 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":"The conference was held virtually.","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)"}}]}}