{"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/bipartite-graph-network-with-adaptive-message","title":"Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation","arxiv_id":"2104.00308","date":"2021-04-01","proceeding":"CVPR 2021 1","authors":["Rongjie Li","Songyang Zhang","Bo Wan","Xuming He"],"abstract":"Scene graph generation is an important visual understanding task with a broad range of vision applications. Despite recent tremendous progress, it remains challenging due to the intrinsic long-tailed class distribution and large intra-class variation. To address these issues, we introduce a novel confidence-aware bipartite graph neural network with adaptive message propagation mechanism for unbiased scene graph generation. In addition, we propose an efficient bi-level data resampling strategy to alleviate the imbalanced data distribution problem in training our graph network. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including Visual Genome, Open Images V4/V6, demonstrating its effectiveness and generality.","url_abs":"https://arxiv.org/abs/2104.00308v2","url_pdf":"https://arxiv.org/pdf/2104.00308v2.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":"bipartite-graph-network-with-adaptive-message","repo_url":"https://github.com/Scarecrow0/BGNN-SGG","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"bipartite-graph-network-with-adaptive-message","repo_url":"https://github.com/jeonjaehyeong/dpl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"bipartite-graph-network-with-adaptive-message","repo_url":"https://github.com/rafa-cxg/PySGG-cxg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"bipartite-graph-network-with-adaptive-message","repo_url":"https://github.com/shtuplus/pysgg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"graph-generation","task_name":"Graph Generation"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"scene-graph-generation","task_name":"Scene Graph Generation"},{"task_slug":"unbiased-scene-graph-generation","task_name":"Unbiased Scene Graph Generation"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2104.00308","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}