{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:Y2IG2KHCLHMG3MHGMXZZLS53EH","short_pith_number":"pith:Y2IG2KHC","schema_version":"1.0","canonical_sha256":"c6906d28e259d86db0e665f395cbbb21f03bec06f4d59979c0253a76ae23fda8","source":{"kind":"arxiv","id":"2103.16083","version":1},"attestation_state":"computed","paper":{"title":"Fully Convolutional Scene Graph Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bir Bhanu, Hengyue Liu, Masood S. Mortazavi, Ning Yan","submitted_at":"2021-03-30T05:25:38Z","abstract_excerpt":"This paper presents a fully convolutional scene graph generation (FCSGG) model that detects objects and relations simultaneously. Most of the scene graph generation frameworks use a pre-trained two-stage object detector, like Faster R-CNN, and build scene graphs using bounding box features. Such pipeline usually has a large number of parameters and low inference speed. Unlike these approaches, FCSGG is a conceptually elegant and efficient bottom-up approach that encodes objects as bounding box center points, and relationships as 2D vector fields which are named as Relation Affinity Fields (RAF"},"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":"2103.16083","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-30T05:25:38Z","cross_cats_sorted":[],"title_canon_sha256":"50727d33d13a47e0afa35b08d09430cb2b97a097d2c35cc68134b7223e64d99b","abstract_canon_sha256":"39b9d57730c1672e2769a5baf83f805dda8f81c317251729e06bcbad4153cec2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:27:48.084114Z","signature_b64":"1ngy+OTybzJsEH8o4P8MyfxHxSyWyThjIfRqEbKxZPEK50JKrzYDwsvC4XHV9NWcBCLW3DuE+37YgN4qUazeDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6906d28e259d86db0e665f395cbbb21f03bec06f4d59979c0253a76ae23fda8","last_reissued_at":"2026-07-05T02:27:48.083623Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:27:48.083623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fully Convolutional Scene Graph Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bir Bhanu, Hengyue Liu, Masood S. Mortazavi, Ning Yan","submitted_at":"2021-03-30T05:25:38Z","abstract_excerpt":"This paper presents a fully convolutional scene graph generation (FCSGG) model that detects objects and relations simultaneously. Most of the scene graph generation frameworks use a pre-trained two-stage object detector, like Faster R-CNN, and build scene graphs using bounding box features. Such pipeline usually has a large number of parameters and low inference speed. Unlike these approaches, FCSGG is a conceptually elegant and efficient bottom-up approach that encodes objects as bounding box center points, and relationships as 2D vector fields which are named as Relation Affinity Fields (RAF"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.16083","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.16083/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2103.16083","created_at":"2026-07-05T02:27:48.083697+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.16083v1","created_at":"2026-07-05T02:27:48.083697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.16083","created_at":"2026-07-05T02:27:48.083697+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y2IG2KHCLHMG","created_at":"2026-07-05T02:27:48.083697+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y2IG2KHCLHMG3MHG","created_at":"2026-07-05T02:27:48.083697+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y2IG2KHC","created_at":"2026-07-05T02:27:48.083697+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/Y2IG2KHCLHMG3MHGMXZZLS53EH","json":"https://pith.science/pith/Y2IG2KHCLHMG3MHGMXZZLS53EH.json","graph_json":"https://pith.science/api/pith-number/Y2IG2KHCLHMG3MHGMXZZLS53EH/graph.json","events_json":"https://pith.science/api/pith-number/Y2IG2KHCLHMG3MHGMXZZLS53EH/events.json","paper":"https://pith.science/paper/Y2IG2KHC"},"agent_actions":{"view_html":"https://pith.science/pith/Y2IG2KHCLHMG3MHGMXZZLS53EH","download_json":"https://pith.science/pith/Y2IG2KHCLHMG3MHGMXZZLS53EH.json","view_paper":"https://pith.science/paper/Y2IG2KHC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.16083&json=true","fetch_graph":"https://pith.science/api/pith-number/Y2IG2KHCLHMG3MHGMXZZLS53EH/graph.json","fetch_events":"https://pith.science/api/pith-number/Y2IG2KHCLHMG3MHGMXZZLS53EH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y2IG2KHCLHMG3MHGMXZZLS53EH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y2IG2KHCLHMG3MHGMXZZLS53EH/action/storage_attestation","attest_author":"https://pith.science/pith/Y2IG2KHCLHMG3MHGMXZZLS53EH/action/author_attestation","sign_citation":"https://pith.science/pith/Y2IG2KHCLHMG3MHGMXZZLS53EH/action/citation_signature","submit_replication":"https://pith.science/pith/Y2IG2KHCLHMG3MHGMXZZLS53EH/action/replication_record"}},"created_at":"2026-07-05T02:27:48.083697+00:00","updated_at":"2026-07-05T02:27:48.083697+00:00"}