{"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/pdnet-prior-model-guided-depth-enhanced","title":"PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection","arxiv_id":"1803.08636","date":"2018-03-23","proceeding":null,"authors":["Chunbiao Zhu","Xing Cai","Kan Huang","Thomas H. Li","Ge Li"],"abstract":"Fully convolutional neural networks (FCNs) have shown outstanding performance\nin many computer vision tasks including salient object detection. However,\nthere still remains two issues needed to be addressed in deep learning based\nsaliency detection. One is the lack of tremendous amount of annotated data to\ntrain a network. The other is the lack of robustness for extracting salient\nobjects in images containing complex scenes. In this paper, we present a new\narchitecture$ - $PDNet, a robust prior-model guided depth-enhanced network for\nRGB-D salient object detection. In contrast to existing works, in which RGB-D\nvalues of image pixels are fed directly to a network, the proposed architecture\nis composed of a master network for processing RGB values, and a sub-network\nmaking full use of depth cues and incorporate depth-based features into the\nmaster network. To overcome the limited size of the labeled RGB-D dataset for\ntraining, we employ a large conventional RGB dataset to pre-train the master\nnetwork, which proves to contribute largely to the final accuracy. Extensive\nevaluations over five benchmark datasets demonstrate that our proposed method\nperforms favorably against the state-of-the-art approaches.","url_abs":"http://arxiv.org/abs/1803.08636v2","url_pdf":"http://arxiv.org/pdf/1803.08636v2.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":"pdnet-prior-model-guided-depth-enhanced","repo_url":"https://github.com/ChunbiaoZhu/PDNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"pdnet-prior-model-guided-depth-enhanced","repo_url":"https://github.com/cai199626/PDNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"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":"saliency-detection","task_name":"Saliency 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":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.08636","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}