{"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/neural-message-passing-for-visual","title":"Neural Message Passing for Visual Relationship Detection","arxiv_id":"2208.04165","date":"2022-08-08","proceeding":null,"authors":["Yue Hu","Siheng Chen","Xu Chen","Ya zhang","Xiao Gu"],"abstract":"Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and interactions. Since the interactions associated with the same object are dependent, we explore the dependency of interactions to reduce the search space. We explicitly model objects and interactions by an interaction graph and then propose a message-passing-style algorithm to propagate the contextual information. We thus call the proposed method neural message passing (NMP). We further integrate language priors and spatial cues to rule out unrealistic interactions and capture spatial interactions. Experimental results on two benchmark datasets demonstrate the superiority of our proposed method. Our code is available at https://github.com/PhyllisH/NMP.","url_abs":"https://arxiv.org/abs/2208.04165v1","url_pdf":"https://arxiv.org/pdf/2208.04165v1.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":"neural-message-passing-for-visual","repo_url":"https://github.com/phyllish/nmp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"relationship-detection","task_name":"Relationship Detection"},{"task_slug":"visual-relationship-detection","task_name":"Visual Relationship Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2208.04165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.04165"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/PhyllisH/NMP","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/phyllish/nmp","reach":{"status":"ok"}}],"summary":{"ran":2,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1},"community":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"5fcdd34184610b70","entry":"FC","repo":"phyllish/nmp","repo_kind":"official","path":"modules.py","file_url":"https://github.com/phyllish/nmp/blob/HEAD/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5fcdd34184610b70"}},{"code_sha256_prefix":"09af0e453601fdfa","entry":"MLP","repo":"phyllish/nmp","repo_kind":"official","path":"modules.py","file_url":"https://github.com/phyllish/nmp/blob/HEAD/modules.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"09af0e453601fdfa"}},{"code_sha256_prefix":"06ae24901264ad1a","entry":"restricted_float","repo":"priba/nmp_qc","repo_kind":"community","path":"demos/demo_qm9_duvenaud.py","file_url":"https://github.com/priba/nmp_qc/blob/HEAD/demos/demo_qm9_duvenaud.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"06ae24901264ad1a"}},{"code_sha256_prefix":"07396eeb67d0a994","entry":"NMPEncoder","repo":"phyllish/nmp","repo_kind":"official","path":"modules.py","file_url":"https://github.com/phyllish/nmp/blob/HEAD/modules.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"07396eeb67d0a994"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}