{"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/non-local-deep-features-for-salient-object","title":"Non-Local Deep Features for Salient Object Detection","arxiv_id":null,"date":"2017-07-01","proceeding":"CVPR 2017 7","authors":["Zhiming Luo","Akshaya Mishra","Andrew Achkar","Justin Eichel","Shaozi Li","Pierre-Marc Jodoin"],"abstract":"Saliency detection aims to highlight the most relevant objects in an image.  Methods using conventional models struggle whenever salient objects are pictured on top of a cluttered background while deep neural nets suffer from excess complexity and slow evaluation speeds.  In this paper, we propose a simplified convolutional neural network  which combines local and global information through a multi-resolution 4x5 grid structure. Instead of enforcing spacial coherence with a CRF or superpixels as is usually the case, we implemented a loss function inspired by the Mumford-Shah functional which penalizes errors on the boundary.  We trained our model on the MSRA-B dataset, and tested it on six different saliency benchmark datasets. Results show that our method is on par with the state-of-the-art while reducing computation time by a factor of 18 to 100 times, enabling near real-time, high performance saliency detection.\r","url_abs":"http://openaccess.thecvf.com/content_cvpr_2017/html/Luo_Non-Local_Deep_Features_CVPR_2017_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2017/papers/Luo_Non-Local_Deep_Features_CVPR_2017_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":[{"paper_slug":"non-local-deep-features-for-salient-object","repo_url":"https://github.com/AceCoooool/NLDF-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"non-local-deep-features-for-salient-object","repo_url":"https://github.com/zhimingluo/NLDF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"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":"saliency-detection","task_name":"Saliency 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/salient-object-detection-on-duts-te","task":"RGB Salient Object Detection","dataset":"DUTS-TE","model":"NLDF","rank_in_archive_order":23,"of":31,"metrics":{"MAE":"0.065","max F-measure":"0.816"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-istd","task":"RGB Salient Object Detection","dataset":"ISTD","model":"NLDF","rank_in_archive_order":4,"of":7,"metrics":{"Balanced Error Rate":"7.50"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-sbu","task":"RGB Salient Object Detection","dataset":"SBU / SBU-Refine","model":"NLDF","rank_in_archive_order":5,"of":7,"metrics":{"Balanced Error Rate":"7.02"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-soc","task":"RGB Salient Object Detection","dataset":"SOC","model":"NLDF","rank_in_archive_order":5,"of":7,"metrics":{"Average MAE":"0.106","S-Measure":"0.816","mean E-Measure":"0.837"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-ucf","task":"RGB Salient Object Detection","dataset":"UCF","model":"NLDF","rank_in_archive_order":2,"of":7,"metrics":{"Balanced Error Rate":"7.69"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}