{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BKBUDSPKE4VOYVC6I42CEWAC2C","short_pith_number":"pith:BKBUDSPK","schema_version":"1.0","canonical_sha256":"0a8341c9ea272aec545e4734225802d0bb5eb74c09fd994499dfbe8a6fa6980c","source":{"kind":"arxiv","id":"1904.00560","version":1},"attestation_state":"computed","paper":{"title":"Scene Graph Generation with External Knowledge and Image Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Handong Zhao, Jianfei Cai, Jiuxiang Gu, Mingyang Ling, Sheng Li, Zhe Lin","submitted_at":"2019-04-01T04:37:35Z","abstract_excerpt":"Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction,~\\etc. However, existing datasets are biased in terms of object and relationship labels, or often come with noisy and missing annotations, which makes the development of a reliable scene graph prediction model very challenging. In this paper, we propose a novel scene graph generation algorithm with external knowledge and image reconstruction loss to overcome these dataset issues. In particular, we extract commonsense knowledge"},"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":"1904.00560","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-01T04:37:35Z","cross_cats_sorted":[],"title_canon_sha256":"568b65fb245f3782d869a744296bb48f266b4eb1a18ae7612e6b2e803c8100c6","abstract_canon_sha256":"00e51ee1d0af7b5bbbb3fd0e987d1e15af15938ae7a289c054475a8a31900766"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:48.073632Z","signature_b64":"qMJX7UbU/j1dLs6Whe85o+gM6JIJY1HwrFMk9HW7z9yCMVOogppUxhcbKL+6QEHQQfnqAGv8SK77MFQA5XnnDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a8341c9ea272aec545e4734225802d0bb5eb74c09fd994499dfbe8a6fa6980c","last_reissued_at":"2026-05-17T23:49:48.072881Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:48.072881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scene Graph Generation with External Knowledge and Image Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Handong Zhao, Jianfei Cai, Jiuxiang Gu, Mingyang Ling, Sheng Li, Zhe Lin","submitted_at":"2019-04-01T04:37:35Z","abstract_excerpt":"Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction,~\\etc. However, existing datasets are biased in terms of object and relationship labels, or often come with noisy and missing annotations, which makes the development of a reliable scene graph prediction model very challenging. In this paper, we propose a novel scene graph generation algorithm with external knowledge and image reconstruction loss to overcome these dataset issues. In particular, we extract commonsense knowledge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00560","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":"1904.00560","created_at":"2026-05-17T23:49:48.073005+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.00560v1","created_at":"2026-05-17T23:49:48.073005+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00560","created_at":"2026-05-17T23:49:48.073005+00:00"},{"alias_kind":"pith_short_12","alias_value":"BKBUDSPKE4VO","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"BKBUDSPKE4VOYVC6","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"BKBUDSPK","created_at":"2026-05-18T12:33:12.712433+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/BKBUDSPKE4VOYVC6I42CEWAC2C","json":"https://pith.science/pith/BKBUDSPKE4VOYVC6I42CEWAC2C.json","graph_json":"https://pith.science/api/pith-number/BKBUDSPKE4VOYVC6I42CEWAC2C/graph.json","events_json":"https://pith.science/api/pith-number/BKBUDSPKE4VOYVC6I42CEWAC2C/events.json","paper":"https://pith.science/paper/BKBUDSPK"},"agent_actions":{"view_html":"https://pith.science/pith/BKBUDSPKE4VOYVC6I42CEWAC2C","download_json":"https://pith.science/pith/BKBUDSPKE4VOYVC6I42CEWAC2C.json","view_paper":"https://pith.science/paper/BKBUDSPK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.00560&json=true","fetch_graph":"https://pith.science/api/pith-number/BKBUDSPKE4VOYVC6I42CEWAC2C/graph.json","fetch_events":"https://pith.science/api/pith-number/BKBUDSPKE4VOYVC6I42CEWAC2C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BKBUDSPKE4VOYVC6I42CEWAC2C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BKBUDSPKE4VOYVC6I42CEWAC2C/action/storage_attestation","attest_author":"https://pith.science/pith/BKBUDSPKE4VOYVC6I42CEWAC2C/action/author_attestation","sign_citation":"https://pith.science/pith/BKBUDSPKE4VOYVC6I42CEWAC2C/action/citation_signature","submit_replication":"https://pith.science/pith/BKBUDSPKE4VOYVC6I42CEWAC2C/action/replication_record"}},"created_at":"2026-05-17T23:49:48.073005+00:00","updated_at":"2026-05-17T23:49:48.073005+00:00"}