{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T19:47:42Z","timestamp":1743104862502,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819947607"},{"type":"electronic","value":"9789819947614"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-4761-4_6","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T16:02:10Z","timestamp":1690732930000},"page":"63-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Simple Mixed-Supervised Learning Method for Salient Object Detection"],"prefix":"10.1007","author":[{"given":"Congjin","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyu","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"6_CR1","unstructured":"Yun, Y.K., Lin, W.: Selfreformer: Self-refined network with transformer for salient object detection. arXiv preprint arXiv:2205.11283 (2022)"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"6_CR3","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","volume":"8","author":"W Wang","year":"2022","unstructured":"Wang, W., et al.: Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media 8(3), 415\u2013424 (2022)","journal-title":"Computational Visual Media"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Liu, N., Zhang, N., Wan, K., Shao, L., Han, J.: Visual saliency transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 4722\u20134732 (2021)","DOI":"10.1109\/ICCV48922.2021.00468"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q.: Decomposition and completion network for salient object detection. IEEE Trans. Image Process. 30, 6226\u20136239 (2021)","DOI":"10.1109\/TIP.2021.3093380"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Piao, Y., Wang, J., Zhang, M., Lu, H.: Mfnet: Multi-filter directive network for weakly supervised salient object detection, pp. 4136\u20134145 (2021)","DOI":"10.1109\/ICCV48922.2021.00410"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, P., Cao, Y., Liang, Z., Lau, R.W.H.: Weakly- supervised salient object detection with saliency bounding boxes. IEEE Transac- tions on Image Processing 30, 4423\u20134435 (2021)","DOI":"10.1109\/TIP.2021.3071691"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yu, X., Li, A., Song, P., Liu, B., Dai, Y.: Weakly- supervised salient object detection via scribble annotations, pp. 12546\u201312555 (2020)","DOI":"10.1109\/CVPR42600.2020.01256"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Gao, S., et al.: Weakly-supervised salient object detection using point supervison. National conference on artificial intelligence (2022)","DOI":"10.1145\/3503161.3547912"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Yu, S., Zhang, B., Xiao, J., Lim, E.G.: Structure-consistent weakly supervised salient object detection with local saliency coherence 35(4), 3234\u20133242 (2021)","DOI":"10.1609\/aaai.v35i4.16434"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Wang, X., Al-Huda, Z., Peng, B.: Weakly-supervised salient object detection through object segmentation guided by scribble annotations. In: 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 304\u2013312. IEEE (2021)","DOI":"10.1109\/ISKE54062.2021.9755333"},{"key":"6_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102515","volume":"80","author":"F Gao","year":"2022","unstructured":"Gao, F., et al.: Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images. Med. Image Anal. 80, 102515 (2022)","journal-title":"Med. Image Anal."},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Cong, R., et al.: A weakly supervised learning framework for salient object detection via hybrid labels. IEEE (2022)","DOI":"10.1109\/TCSVT.2022.3205182"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Xiang, T.-Z., Zhang, L., Lu, H.: Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2160\u20132170 (2022)","DOI":"10.1109\/CVPR52688.2022.00220"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Zhou, B., Yang, G., Wan, X., Wang, Y., Liu, C., Wang, H.: A simple network with progressive structure for salient object detection, pp. 397\u2013408 (2021)","DOI":"10.1007\/978-3-030-88007-1_33"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Zhao, K., Gao, S., Wang, W., Cheng, M.-M.: Optimizing the f-measure for threshold-free salient object detection, pp. 8849\u20138857 (2019)","DOI":"10.1109\/ICCV.2019.00894"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Learning to detect salient objects with image-level supervision, pp. 136\u2013145 (2017)","DOI":"10.1109\/CVPR.2017.404"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.-H.: Saliency detection via graph-based manifold ranking, pp. 3166\u20133173 (2013)","DOI":"10.1109\/CVPR.2013.407"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection, pp. 1155\u20131162 (2013)","DOI":"10.1109\/CVPR.2013.153"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation, pp. 280\u2013287 (2014)","DOI":"10.1109\/CVPR.2014.43"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Li, G., Yu, Y.: Visual saliency based on multiscale deep features, pp. 5455\u20135463 (2015)","DOI":"10.1109\/CVPR.2015.7299184"},{"key":"6_CR23","unstructured":"Ran, M., Lihi, Z.-M., Tal, A.: How to evaluate foreground maps? In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 248\u2013255 (2014)"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Philipp, K., Pritch, Y., Hornung, A.: 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":"6_CR25","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., Borji, A.: 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":"6_CR26","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. In: European conference on computer vision, pp. 35\u201351. Springer (2020)","DOI":"10.1007\/978-3-030-58536-5_3"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Zhang, L., Lu, H.: 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"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O.R., Jagersand, M.: U2-net: Going deeper with nested u-structure for salient object detection. Pattern recognition 106, 107404 (2020)","DOI":"10.1016\/j.patcog.2020.107404"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Piao, Y., Wu, W, Zhang, M., Jiang, Y., Lu, H.: Noise- sensitive adversarial learning for weakly supervised salient object detection. IEEE Transactions on Multimedia (2022)","DOI":"10.1109\/TMM.2022.3152567"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Ji, G.-P., Sun, G., Cheng, M.-M., Shen, J., Shao, L.: Camouflaged object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 2777\u20132787 (2020)","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Le, T.-N., Nguyen, T.V., Nie, Z., Tran, M., Sugimoto, A.: Anabranch network for camouflaged object segmentation. Computer Vision and Image Understanding 184, 45\u201356 (2019)","DOI":"10.1016\/j.cviu.2019.04.006"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, H., Rong, Z., Liu, L., Su, H.: Scribble- supervised segmentation of aerial building footprints using adversarial learning. IEEE Access 6, 58898\u201358911 (2018)","DOI":"10.1109\/ACCESS.2018.2874544"},{"key":"6_CR33","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.isprsjprs.2022.07.014","volume":"191","author":"Z Huang","year":"2022","unstructured":"Huang, Z., Xiang, T.-Z., Chen, H.-X., Dai, H.: Scribble-based boundary-aware network for weakly supervised salient object detection in remote sensing images. ISPRS J. Photogramm. Remote. Sens. 191, 290\u2013301 (2022)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"He, R., Dong, Q., Lin, J., Lau, R.W.H.: Weakly-supervised camouflaged object detection with scribble annotations. arXiv preprint arXiv:2207.14083 (2022)","DOI":"10.1609\/aaai.v37i1.25156"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4761-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T09:22:06Z","timestamp":1729848126000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4761-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947607","9789819947614"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4761-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}