{"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/domain-separation-graph-neural-networks-for","title":"Domain Separation Graph Neural Networks for Saliency Object Ranking","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Zijian Wu","Jun Lu","Jing Han","Lianfa Bai","Yi Zhang","Zhuang Zhao","Siyang Song"],"abstract":"    Saliency object ranking (SOR) has attracted significant attention recently. Previous methods usually failed to explicitly explore the saliency degree-related relationships between objects. In this paper we propose a novel Domain Separation Graph Neural Network (DSGNN) which starts with separately extracting the shape and texture cues from each object and builds an shape graph as well as a texture graph for all objects in the given image. Then we propose a Shape-Texture Graph Domain Separation (STGDS) module to separate the task-relevant and irrelevant information of target objects by explicitly modelling the relationship between each pair of objects in terms of their shapes and textures respectively. Furthermore a Cross Image Graph Domain Separation (CIGDS) module is introduced to explore the saliency degree subspace that is robust to different scenes aiming to create a unified representation for targets with the same saliency levels in different images. Importantly our DSGNN automatically learns a multi-dimensional feature to represent each graph edge allowing complex diverse and ranking-related relationships to be modelled. Experimental results show that our DSGNN achieved the new state-of-the-art performance on both ASSR and IRSR datasets with large improvements of 5.2% and 4.1% SA-SOR respectively. Our code is provided in https://github.com/Wu-ZJ/DSGNN.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Wu_Domain_Separation_Graph_Neural_Networks_for_Saliency_Object_Ranking_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Wu_Domain_Separation_Graph_Neural_Networks_for_Saliency_Object_Ranking_CVPR_2024_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":"domain-separation-graph-neural-networks-for","repo_url":"https://github.com/wu-zj/dsgnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}