{"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/deeply-supervised-salient-object-detection","title":"Deeply supervised salient object detection with short connections","arxiv_id":"1611.04849","date":"2016-11-15","proceeding":"CVPR 2017 7","authors":["Qibin Hou","Ming-Ming Cheng","Xiao-Wei Hu","Ali Borji","Zhuowen Tu","Philip Torr"],"abstract":"Recent progress on saliency detection is substantial, benefiting mostly from\nthe explosive development of Convolutional Neural Networks (CNNs). Semantic\nsegmentation and saliency detection algorithms developed lately have been\nmostly based on Fully Convolutional Neural Networks (FCNs). There is still a\nlarge room for improvement over the generic FCN models that do not explicitly\ndeal with the scale-space problem. Holistically-Nested Edge Detector (HED)\nprovides a skip-layer structure with deep supervision for edge and boundary\ndetection, but the performance gain of HED on salience detection is not\nobvious. In this paper, we propose a new method for saliency detection by\nintroducing short connections to the skip-layer structures within the HED\narchitecture. Our framework provides rich multi-scale feature maps at each\nlayer, a property that is critically needed to perform segment detection. Our\nmethod produces state-of-the-art results on 5 widely tested salient object\ndetection benchmarks, with advantages in terms of efficiency (0.15 seconds per\nimage), effectiveness, and simplicity over the existing algorithms.","url_abs":"http://arxiv.org/abs/1611.04849v4","url_pdf":"http://arxiv.org/pdf/1611.04849v4.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":[{"paper_slug":"deeply-supervised-salient-object-detection","repo_url":"https://github.com/AceCoooool/DSS-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deeply-supervised-salient-object-detection","repo_url":"https://github.com/Andrew-Qibin/DSS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deeply-supervised-salient-object-detection","repo_url":"https://github.com/houqb/DSS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deeply-supervised-salient-object-detection","repo_url":"https://github.com/moothes/salod","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"boundary-detection","task_name":"Boundary Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"salient-object-detection","task_name":"RGB Salient Object Detection"},{"task_slug":"saliency-detection","task_name":"Saliency Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcn","method_name":"FCN"},{"method_slug":"max-pooling","method_name":"Max Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/salient-object-detection-on-duts-te","task":"RGB Salient Object Detection","dataset":"DUTS-TE","model":"DSS","rank_in_archive_order":25,"of":31,"metrics":{"MAE":"0.065","max F-measure":"0.813"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-istd","task":"RGB Salient Object Detection","dataset":"ISTD","model":"DSS","rank_in_archive_order":7,"of":7,"metrics":{"Balanced Error Rate":"10.48"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-sbu","task":"RGB Salient Object Detection","dataset":"SBU / SBU-Refine","model":"DSS","rank_in_archive_order":4,"of":7,"metrics":{"Balanced Error Rate":"7.00"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-ucf","task":"RGB Salient Object Detection","dataset":"UCF","model":"DSS","rank_in_archive_order":5,"of":7,"metrics":{"Balanced Error Rate":"10.56"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.04849","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.04849"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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