{"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/spsn-superpixel-prototype-sampling-network","title":"SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection","arxiv_id":"2207.07898","date":"2022-07-16","proceeding":null,"authors":["Minhyeok Lee","Chaewon Park","Suhwan Cho","Sangyoun Lee"],"abstract":"RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large domain gap between an RGB image and the depth map and low-quality depth maps. To solve this problem, we propose a novel superpixel prototype sampling network (SPSN) architecture. The proposed model splits the input RGB image and depth map into component superpixels to generate component prototypes. We design a prototype sampling network so that the network only samples prototypes corresponding to salient objects. In addition, we propose a reliance selection module to recognize the quality of each RGB and depth feature map and adaptively weight them in proportion to their reliability. The proposed method makes the model robust to inconsistencies between RGB images and depth maps and eliminates the influence of non-salient objects. Our method is evaluated on five popular datasets, achieving state-of-the-art performance. We prove the effectiveness of the proposed method through comparative experiments.","url_abs":"https://arxiv.org/abs/2207.07898v1","url_pdf":"https://arxiv.org/pdf/2207.07898v1.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":"spsn-superpixel-prototype-sampling-network","repo_url":"https://github.com/Hydragon516/SPSN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object-detection","task_name":"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":"superpixels","task_name":"Superpixels"},{"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":"SPSN","rank_in_archive_order":4,"of":13,"metrics":{"Average MAE":"0.016","S-Measure":"93.8","max E-Measure":"97.6","max F-Measure":"94.3"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-d-salient-object-detection-on-nju2k","task":"RGB-D Salient Object Detection","dataset":"NJU2K","model":"SPSN","rank_in_archive_order":4,"of":27,"metrics":{"Average MAE":"0.032","S-Measure":"91.8","max E-Measure":"95.0","max F-Measure":"92.0"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-d-salient-object-detection-on-nlpr","task":"RGB-D Salient Object Detection","dataset":"NLPR","model":"SPSN","rank_in_archive_order":5,"of":14,"metrics":{"Average MAE":"0.022","S-Measure":"92.6","max E-Measure":"96.2","max F-Measure":"91.4"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-d-salient-object-detection-on-sip","task":"RGB-D Salient Object Detection","dataset":"SIP","model":"SPSN","rank_in_archive_order":5,"of":16,"metrics":{"Average MAE":"0.042","S-Measure":"89.2","max E-Measure":"93.4","max F-Measure":"89.9"},"uses_additional_data":false},{"leaderboard":"/sota/rgb-d-salient-object-detection-on-stere","task":"RGB-D Salient Object Detection","dataset":"STERE","model":"SPSN","rank_in_archive_order":7,"of":14,"metrics":{"Average MAE":"0.035","S-Measure":"90.7","max E-Measure":"94.3","max F-Measure":"90.0"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2207.07898","atlas_url":"https://app.syntology.ai/?focus=2207.07898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07898"}},"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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