{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/co-saliency-detection-via-mask-guided-fully","title":"Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing","arxiv_id":null,"date":"2019-06-01","proceeding":"CVPR 2019 6","authors":["Kaihua Zhang"," Tengpeng Li"," Bo Liu"," Qingshan Liu"],"abstract":"In image co-saliency detection problem, one critical issue is how to model the concurrent pattern of the co-salient parts, which appears both within each image and across all the relevant images. In this paper, we propose a hierarchical image co-saliency detection framework as a coarse to fine strategy to capture this pattern. We first propose a mask-guided fully convolutional network structure to generate the initial co-saliency detection result. The mask is used for background removal and it is learned from the high-level feature response maps of the pre-trained VGG-net output. We next propose a multi-scale label smoothing model to further refine the detection result. The proposed model jointly optimizes the label smoothness of pixels and superpixels. Experiment results on three popular image co-saliency detection benchmark datasets including iCoseg, MSRC and Cosal2015 demonstrate the remarkable performance compared with the state-of-the-art methods.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Co-Saliency_Detection_via_Mask-Guided_Fully_Convolutional_Networks_With_Multi-Scale_Label_CVPR_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Co-Saliency_Detection_via_Mask-Guided_Fully_Convolutional_Networks_With_Multi-Scale_Label_CVPR_2019_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"co-saliency-detection","task_name":"Co-Salient Object Detection"},{"task_slug":"saliency-detection","task_name":"Saliency Detection"},{"task_slug":"superpixels","task_name":"Superpixels"}],"methods":[{"method_slug":"label-smoothing","method_name":"Label Smoothing"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/co-salient-object-detection-on-coca","task":"Co-Salient Object Detection","dataset":"CoCA","model":"CSMG","rank_in_archive_order":9,"of":10,"metrics":{"Mean F-measure":"0.390","S-measure":"0.627","max F-measure":"0.499","mean E-measure":"0.606"},"uses_additional_data":false},{"leaderboard":"/sota/co-salient-object-detection-on-cosod3k","task":"Co-Salient Object Detection","dataset":"CoSOD3k","model":"CSMG","rank_in_archive_order":9,"of":10,"metrics":{"MAE":"0.157","S-measure":"0.711","max E-measure":"0.804","max F-measure":"0.709"},"uses_additional_data":false},{"leaderboard":"/sota/co-salient-object-detection-on-cosal2015","task":"Co-Salient Object Detection","dataset":"CoSal2015","model":"CSMG","rank_in_archive_order":10,"of":10,"metrics":{"MAE":"0.130","S-measure":"0.774","max E-measure":"0.842","max F-measure":"0.784"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}