{"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/adaptive-fusion-for-rgb-d-salient-object","title":"Adaptive Fusion for RGB-D Salient Object Detection","arxiv_id":"1901.01369","date":"2019-01-05","proceeding":null,"authors":["Ningning Wang","Xiaojin Gong"],"abstract":"RGB-D salient object detection aims to identify the most visually distinctive\nobjects in a pair of color and depth images. Based upon an observation that\nmost of the salient objects may stand out at least in one modality, this paper\nproposes an adaptive fusion scheme to fuse saliency predictions generated from\ntwo modalities. Specifically, we design a two-streamed convolutional neural\nnetwork (CNN), each of which extracts features and predicts a saliency map from\neither RGB or depth modality. Then, a saliency fusion module learns a switch\nmap that is used to adaptively fuse the predicted saliency maps. A loss\nfunction composed of saliency supervision, switch map supervision, and\nedge-preserving constraints is designed to make full supervision, and the\nentire network is trained in an end-to-end manner. Benefited from the adaptive\nfusion strategy and the edge-preserving constraint, our approach outperforms\nstate-of-the-art methods on three publicly available datasets.","url_abs":"http://arxiv.org/abs/1901.01369v2","url_pdf":"http://arxiv.org/pdf/1901.01369v2.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":"adaptive-fusion-for-rgb-d-salient-object","repo_url":"https://github.com/Lucia-Ningning/Adaptive_Fusion_RGBD_Saliency_Detection","is_official":1,"mentioned_in_paper":1,"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":"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":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1901.01369","atlas_url":"https://app.syntology.ai/?focus=1901.01369","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}