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In this work, we explicitly model the objects and\ntheir relationships using scene graphs, a visually-grounded graphical structure\nof an image. We propose a novel end-to-end model that generates such structured\nscene representation from an input image. The model solves the scene graph\ninference problem using standard RNNs and learns to iteratively improves its\npredictions via message passing. Our joint inference model can take advantage\nof contextual cues to make better predictions on objects and their\nrelationships. The experiments show that our model significantly outperforms\nprevious methods for generating scene graphs using Visual Genome dataset and\ninferring support relations with NYU Depth v2 dataset.","url_abs":"http://arxiv.org/abs/1701.02426v2","url_pdf":"http://arxiv.org/pdf/1701.02426v2.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":"scene-graph-generation-by-iterative-message","repo_url":"https://github.com/bknyaz/sgg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"scene-graph-generation-by-iterative-message","repo_url":"https://github.com/joshuafeinglass/vl-detector-eval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"scene-graph-generation-by-iterative-message","repo_url":"https://github.com/microsoft/scene_graph_benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"scene-graph-generation-by-iterative-message","repo_url":"https://github.com/shikorab/SceneGraph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"scene-graph-generation-by-iterative-message","repo_url":"https://github.com/zhangce01/HiKER-SGG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"graph-generation","task_name":"Graph Generation"},{"task_slug":"panoptic-scene-graph-generation","task_name":"Panoptic Scene Graph Generation"},{"task_slug":"scene-graph-generation","task_name":"Scene Graph Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/panoptic-scene-graph-generation-on-psg","task":"Panoptic Scene Graph Generation","dataset":"PSG Dataset","model":"IMP","rank_in_archive_order":9,"of":9,"metrics":{"R@20":"16.5","mR@20":"6.52"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.02426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.02426"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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