{"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/towards-high-resolution-salient-object","title":"Towards High-Resolution Salient Object Detection","arxiv_id":"1908.07274","date":"2019-08-20","proceeding":"ICCV 2019 10","authors":["Yi Zeng","Pingping Zhang","Jianming Zhang","Zhe Lin","Huchuan Lu"],"abstract":"Deep neural network based methods have made a significant breakthrough in salient object detection. However, they are typically limited to input images with low resolutions ($400\\times400$ pixels or less). Little effort has been made to train deep neural networks to directly handle salient object detection in very high-resolution images. This paper pushes forward high-resolution saliency detection, and contributes a new dataset, named High-Resolution Salient Object Detection (HRSOD). To our best knowledge, HRSOD is the first high-resolution saliency detection dataset to date. As another contribution, we also propose a novel approach, which incorporates both global semantic information and local high-resolution details, to address this challenging task. More specifically, our approach consists of a Global Semantic Network (GSN), a Local Refinement Network (LRN) and a Global-Local Fusion Network (GLFN). GSN extracts the global semantic information based on down-sampled entire image. Guided by the results of GSN, LRN focuses on some local regions and progressively produces high-resolution predictions. GLFN is further proposed to enforce spatial consistency and boost performance. Experiments illustrate that our method outperforms existing state-of-the-art methods on high-resolution saliency datasets by a large margin, and achieves comparable or even better performance than them on widely-used saliency benchmarks. The HRSOD dataset is available at https://github.com/yi94code/HRSOD.","url_abs":"https://arxiv.org/abs/1908.07274v1","url_pdf":"https://arxiv.org/pdf/1908.07274v1.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":"towards-high-resolution-salient-object","repo_url":"https://github.com/yi94code/HRSOD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"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":"high","task_name":"Vocal Bursts Intensity Prediction"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"davis-s","name":"DAVIS-S","full_name":""},{"slug":"hrsod","name":"HRSOD","full_name":"High-Resolution Salient Object Detection"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/rgb-salient-object-detection-on-davis-s","task":"RGB Salient Object Detection","dataset":"DAVIS-S","model":"Zeng et al. (HRSOD)","rank_in_archive_order":12,"of":12,"metrics":{"F-measure":"0.889","MAE":"0.026","S-measure":"0.876","mBA":"0.618"},"uses_additional_data":true},{"leaderboard":"/sota/rgb-salient-object-detection-on-hrsod","task":"RGB Salient Object Detection","dataset":"HRSOD","model":"Zeng et al.","rank_in_archive_order":14,"of":14,"metrics":{"MAE":"0.030","S-Measure":"0.892","mBA":"0.693","max F-Measure":"0.892"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1908.07274","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}