{"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/deep-contrast-learning-for-salient-object","title":"Deep Contrast Learning for Salient Object Detection","arxiv_id":"1603.01976","date":"2016-03-07","proceeding":"CVPR 2016 6","authors":["Guanbin Li","Yizhou Yu"],"abstract":"Salient object detection has recently witnessed substantial progress due to\npowerful features extracted using deep convolutional neural networks (CNNs).\nHowever, existing CNN-based methods operate at the patch level instead of the\npixel level. Resulting saliency maps are typically blurry, especially near the\nboundary of salient objects. Furthermore, image patches are treated as\nindependent samples even when they are overlapping, giving rise to significant\nredundancy in computation and storage. In this CVPR 2016 paper, we propose an\nend-to-end deep contrast network to overcome the aforementioned limitations.\nOur deep network consists of two complementary components, a pixel-level fully\nconvolutional stream and a segment-wise spatial pooling stream. The first\nstream directly produces a saliency map with pixel-level accuracy from an input\nimage. The second stream extracts segment-wise features very efficiently, and\nbetter models saliency discontinuities along object boundaries. Finally, a\nfully connected CRF model can be optionally incorporated to improve spatial\ncoherence and contour localization in the fused result from these two streams.\nExperimental results demonstrate that our deep model significantly improves the\nstate of the art.","url_abs":"http://arxiv.org/abs/1603.01976v1","url_pdf":"http://arxiv.org/pdf/1603.01976v1.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":[],"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":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"crf","method_name":"CRF"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/salient-object-detection-on-duts-te","task":"RGB Salient Object Detection","dataset":"DUTS-TE","model":"DCL","rank_in_archive_order":26,"of":31,"metrics":{"MAE":"0.081","max F-measure":"0.786"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.01976","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}