{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZMCPBF3EYUP6IXHZ7ZE52BEYHG","short_pith_number":"pith:ZMCPBF3E","schema_version":"1.0","canonical_sha256":"cb04f09764c51fe45cf9fe49dd0498398c8330e089af408f2c4b7a4b0ffc78c9","source":{"kind":"arxiv","id":"1901.03067","version":1},"attestation_state":"computed","paper":{"title":"Multi-Granularity Reasoning for Social Relation Recognition from Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anfu Zhou, Huadong Ma, Meng Zhang, Tao Mei, Wu Liu, Xinchen Liu","submitted_at":"2019-01-10T09:09:44Z","abstract_excerpt":"Discovering social relations in images can make machines better interpret the behavior of human beings. However, automatically recognizing social relations in images is a challenging task due to the significant gap between the domains of visual content and social relation. Existing studies separately process various features such as faces expressions, body appearance, and contextual objects, thus they cannot comprehensively capture the multi-granularity semantics, such as scenes, regional cues of persons, and interactions among persons and objects. To bridge the domain gap, we propose a Multi-"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1901.03067","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-10T09:09:44Z","cross_cats_sorted":[],"title_canon_sha256":"c7d42475a66dcab2be00fe5b6f0524c4867ff6b6654b31329263949dfde1dfec","abstract_canon_sha256":"00a943f205f10a29ecd3986e2edf4b63ac26ba6ff7541ac7c2f07636488268ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:35.639012Z","signature_b64":"DSaWcSLsuV6T2MEvi9wwkxqHgDV/I2HJUYazT3KAdFAmiOMhgeBgs7koAAMzHgn2JPv2Tr91PKkrk32ZWEiLAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb04f09764c51fe45cf9fe49dd0498398c8330e089af408f2c4b7a4b0ffc78c9","last_reissued_at":"2026-05-17T23:56:35.638489Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:35.638489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Granularity Reasoning for Social Relation Recognition from Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anfu Zhou, Huadong Ma, Meng Zhang, Tao Mei, Wu Liu, Xinchen Liu","submitted_at":"2019-01-10T09:09:44Z","abstract_excerpt":"Discovering social relations in images can make machines better interpret the behavior of human beings. However, automatically recognizing social relations in images is a challenging task due to the significant gap between the domains of visual content and social relation. Existing studies separately process various features such as faces expressions, body appearance, and contextual objects, thus they cannot comprehensively capture the multi-granularity semantics, such as scenes, regional cues of persons, and interactions among persons and objects. To bridge the domain gap, we propose a Multi-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03067","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1901.03067","created_at":"2026-05-17T23:56:35.638567+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.03067v1","created_at":"2026-05-17T23:56:35.638567+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03067","created_at":"2026-05-17T23:56:35.638567+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZMCPBF3EYUP6","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZMCPBF3EYUP6IXHZ","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZMCPBF3E","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZMCPBF3EYUP6IXHZ7ZE52BEYHG","json":"https://pith.science/pith/ZMCPBF3EYUP6IXHZ7ZE52BEYHG.json","graph_json":"https://pith.science/api/pith-number/ZMCPBF3EYUP6IXHZ7ZE52BEYHG/graph.json","events_json":"https://pith.science/api/pith-number/ZMCPBF3EYUP6IXHZ7ZE52BEYHG/events.json","paper":"https://pith.science/paper/ZMCPBF3E"},"agent_actions":{"view_html":"https://pith.science/pith/ZMCPBF3EYUP6IXHZ7ZE52BEYHG","download_json":"https://pith.science/pith/ZMCPBF3EYUP6IXHZ7ZE52BEYHG.json","view_paper":"https://pith.science/paper/ZMCPBF3E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.03067&json=true","fetch_graph":"https://pith.science/api/pith-number/ZMCPBF3EYUP6IXHZ7ZE52BEYHG/graph.json","fetch_events":"https://pith.science/api/pith-number/ZMCPBF3EYUP6IXHZ7ZE52BEYHG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZMCPBF3EYUP6IXHZ7ZE52BEYHG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZMCPBF3EYUP6IXHZ7ZE52BEYHG/action/storage_attestation","attest_author":"https://pith.science/pith/ZMCPBF3EYUP6IXHZ7ZE52BEYHG/action/author_attestation","sign_citation":"https://pith.science/pith/ZMCPBF3EYUP6IXHZ7ZE52BEYHG/action/citation_signature","submit_replication":"https://pith.science/pith/ZMCPBF3EYUP6IXHZ7ZE52BEYHG/action/replication_record"}},"created_at":"2026-05-17T23:56:35.638567+00:00","updated_at":"2026-05-17T23:56:35.638567+00:00"}