{"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/bbs-net-rgb-d-salient-object-detection-with-a","title":"Bifurcated backbone strategy for RGB-D salient object detection","arxiv_id":"2007.02713","date":"2020-07-06","proceeding":null,"authors":["Yingjie Zhai","Deng-Ping Fan","Jufeng Yang","Ali Borji","Ling Shao","Junwei Han","Liang Wang"],"abstract":"Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features meet multi-modal cues, the optimal feature aggregation and multi-modal learning strategy become a hot potato. In this paper, we leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to devise a novel cascaded refinement network. In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS). Second, we introduce a depth-enhanced module (DEM) to excavate informative depth cues from the channel and spatial views. Then, RGB and depth modalities are fused in a complementary way. Our architecture, named Bifurcated Backbone Strategy Network (BBS-Net), is simple, efficient, and backbone-independent. Extensive experiments show that BBS-Net significantly outperforms eighteen SOTA models on eight challenging datasets under five evaluation measures, demonstrating the superiority of our approach ($\\sim 4 \\%$ improvement in S-measure $vs.$ the top-ranked model: DMRA-iccv2019). In addition, we provide a comprehensive analysis on the generalization ability of different RGB-D datasets and provide a powerful training set for future research.","url_abs":"https://arxiv.org/abs/2007.02713v3","url_pdf":"https://arxiv.org/pdf/2007.02713v3.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":"bbs-net-rgb-d-salient-object-detection-with-a","repo_url":"https://github.com/zyjwuyan/BBS-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"bbs-net-rgb-d-salient-object-detection-with-a","repo_url":"https://github.com/DengPingFan/BBS-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"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":"rgb-d-salient-object-detection","task_name":"RGB-D Salient Object Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/rgb-d-salient-object-detection-on-des","task":"RGB-D Salient Object Detection","dataset":"DES","model":"BBS-Net","rank_in_archive_order":9,"of":13,"metrics":{"Average MAE":"0.021","S-Measure":"93.3","max E-Measure":"96.6","max F-Measure":"92.7"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-d-salient-object-detection-on-lfsd","task":"RGB-D Salient Object Detection","dataset":"LFSD","model":"BBS-Net","rank_in_archive_order":5,"of":8,"metrics":{"Average MAE":"0.072","S-Measure":"86.4","max E-Measure":"90.1","max F-Measure":"85.8"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-d-salient-object-detection-on-nlpr","task":"RGB-D Salient Object Detection","dataset":"NLPR","model":"BBS-Net","rank_in_archive_order":3,"of":14,"metrics":{"Average MAE":"0.023","S-Measure":"93.0","max E-Measure":"96.1","max F-Measure":"91.8"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-d-salient-object-detection-on-ssd","task":"RGB-D Salient Object Detection","dataset":"RGBD135","model":"BBS-Net","rank_in_archive_order":2,"of":5,"metrics":{"Average MAE":"0.044","S-Measure":"88.2","max E-Measure":"91.9","max F-Measure":"85.9"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-d-salient-object-detection-on-sip","task":"RGB-D Salient Object Detection","dataset":"SIP","model":"BBS-Net","rank_in_archive_order":10,"of":16,"metrics":{"Average MAE":"0.055","S-Measure":"87.9","max E-Measure":"92.2","max F-Measure":"88.3"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-d-salient-object-detection-on-stere","task":"RGB-D Salient Object Detection","dataset":"STERE","model":"BBS-Net","rank_in_archive_order":6,"of":14,"metrics":{"Average MAE":"0.041","S-Measure":"90.8","max E-Measure":"94.2","max F-Measure":"90.3"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2007.02713","atlas_url":"https://app.syntology.ai/?focus=2007.02713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02713"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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