{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CGOGOJSNTBQTGM4KFGQMS53IR7","short_pith_number":"pith:CGOGOJSN","canonical_record":{"source":{"id":"1808.00191","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T06:50:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a8509c1e36db4d5b57e09bd1b490f3c20a38678619fa9f89b0ff4ba57ada0062","abstract_canon_sha256":"d7cd7d5843bf6533ec2072b87e29f2af67551765a31dfc4d9fef73ad337a8dcf"},"schema_version":"1.0"},"canonical_sha256":"119c67264d986133338a29a0c977688fec2a32f12b5245c8449db8b0132fb667","source":{"kind":"arxiv","id":"1808.00191","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.00191","created_at":"2026-05-18T00:09:08Z"},{"alias_kind":"arxiv_version","alias_value":"1808.00191v1","created_at":"2026-05-18T00:09:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.00191","created_at":"2026-05-18T00:09:08Z"},{"alias_kind":"pith_short_12","alias_value":"CGOGOJSNTBQT","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CGOGOJSNTBQTGM4K","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CGOGOJSN","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CGOGOJSNTBQTGM4KFGQMS53IR7","target":"record","payload":{"canonical_record":{"source":{"id":"1808.00191","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T06:50:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a8509c1e36db4d5b57e09bd1b490f3c20a38678619fa9f89b0ff4ba57ada0062","abstract_canon_sha256":"d7cd7d5843bf6533ec2072b87e29f2af67551765a31dfc4d9fef73ad337a8dcf"},"schema_version":"1.0"},"canonical_sha256":"119c67264d986133338a29a0c977688fec2a32f12b5245c8449db8b0132fb667","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:08.466383Z","signature_b64":"SFPPPABZDuyPOe+9QZ2P0gjadIU/2hCikjRmvKcND9pHi2lTEcY7iUOpbHuD90fXq5PydnyDrlADkcHH9u4uAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"119c67264d986133338a29a0c977688fec2a32f12b5245c8449db8b0132fb667","last_reissued_at":"2026-05-18T00:09:08.465977Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:08.465977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.00191","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:09:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HtACmsUn/+SH+22Fmsb2RqqerxIu2DWydtlV3V4x/fNLWe84QqD7mhMANIzWffyEwKXobh3q2hFlAlix+zzpDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T21:32:04.270556Z"},"content_sha256":"b3fe4c9152d80f2be49fd97333a81b2c4e388238a6fd886f0f4060ebe691ecff","schema_version":"1.0","event_id":"sha256:b3fe4c9152d80f2be49fd97333a81b2c4e388238a6fd886f0f4060ebe691ecff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CGOGOJSNTBQTGM4KFGQMS53IR7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph R-CNN for Scene Graph Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Devi Parikh, Dhruv Batra, Jianwei Yang, Jiasen Lu, Stefan Lee","submitted_at":"2018-08-01T06:50:19Z","abstract_excerpt":"We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images. Our model contains a Relation Proposal Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image. We also propose an attentional Graph Convolutional Network (aGCN) that effectively captures contextual information between objects and relations. Finally, we introduce a new evaluation metric that is more holistic and realistic than existing metrics. We report state-of-the-art performanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.00191","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:09:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l3kz0mI6W1nO05kdU4cagCbNBuMBoZB/81A66kZgahwFMLT03rYjIflT/oaTUjp66APRzfM3/JFF65WrRT+WDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T21:32:04.271230Z"},"content_sha256":"e2373042aed806b69e173f8b881ddcc531d9053db74093767cc72829f72b74c5","schema_version":"1.0","event_id":"sha256:e2373042aed806b69e173f8b881ddcc531d9053db74093767cc72829f72b74c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CGOGOJSNTBQTGM4KFGQMS53IR7/bundle.json","state_url":"https://pith.science/pith/CGOGOJSNTBQTGM4KFGQMS53IR7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CGOGOJSNTBQTGM4KFGQMS53IR7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T21:32:04Z","links":{"resolver":"https://pith.science/pith/CGOGOJSNTBQTGM4KFGQMS53IR7","bundle":"https://pith.science/pith/CGOGOJSNTBQTGM4KFGQMS53IR7/bundle.json","state":"https://pith.science/pith/CGOGOJSNTBQTGM4KFGQMS53IR7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CGOGOJSNTBQTGM4KFGQMS53IR7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CGOGOJSNTBQTGM4KFGQMS53IR7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d7cd7d5843bf6533ec2072b87e29f2af67551765a31dfc4d9fef73ad337a8dcf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T06:50:19Z","title_canon_sha256":"a8509c1e36db4d5b57e09bd1b490f3c20a38678619fa9f89b0ff4ba57ada0062"},"schema_version":"1.0","source":{"id":"1808.00191","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.00191","created_at":"2026-05-18T00:09:08Z"},{"alias_kind":"arxiv_version","alias_value":"1808.00191v1","created_at":"2026-05-18T00:09:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.00191","created_at":"2026-05-18T00:09:08Z"},{"alias_kind":"pith_short_12","alias_value":"CGOGOJSNTBQT","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CGOGOJSNTBQTGM4K","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CGOGOJSN","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:e2373042aed806b69e173f8b881ddcc531d9053db74093767cc72829f72b74c5","target":"graph","created_at":"2026-05-18T00:09:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images. Our model contains a Relation Proposal Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image. We also propose an attentional Graph Convolutional Network (aGCN) that effectively captures contextual information between objects and relations. Finally, we introduce a new evaluation metric that is more holistic and realistic than existing metrics. We report state-of-the-art performanc","authors_text":"Devi Parikh, Dhruv Batra, Jianwei Yang, Jiasen Lu, Stefan Lee","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T06:50:19Z","title":"Graph R-CNN for Scene Graph Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.00191","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b3fe4c9152d80f2be49fd97333a81b2c4e388238a6fd886f0f4060ebe691ecff","target":"record","created_at":"2026-05-18T00:09:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d7cd7d5843bf6533ec2072b87e29f2af67551765a31dfc4d9fef73ad337a8dcf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-01T06:50:19Z","title_canon_sha256":"a8509c1e36db4d5b57e09bd1b490f3c20a38678619fa9f89b0ff4ba57ada0062"},"schema_version":"1.0","source":{"id":"1808.00191","kind":"arxiv","version":1}},"canonical_sha256":"119c67264d986133338a29a0c977688fec2a32f12b5245c8449db8b0132fb667","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"119c67264d986133338a29a0c977688fec2a32f12b5245c8449db8b0132fb667","first_computed_at":"2026-05-18T00:09:08.465977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:08.465977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SFPPPABZDuyPOe+9QZ2P0gjadIU/2hCikjRmvKcND9pHi2lTEcY7iUOpbHuD90fXq5PydnyDrlADkcHH9u4uAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:08.466383Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.00191","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b3fe4c9152d80f2be49fd97333a81b2c4e388238a6fd886f0f4060ebe691ecff","sha256:e2373042aed806b69e173f8b881ddcc531d9053db74093767cc72829f72b74c5"],"state_sha256":"8347da03d438dce57eb7236c888d215ed8ffdad8e516d8aa0624a1ab6d880538"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2pmtzPlGj2EQSNVuZlN1gQ1k3TSwNLsfgEyifm1OXWJFjz7Ba88UvC2dRVhMcL94OcNzkULZW3/3TrunoxiRBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T21:32:04.274892Z","bundle_sha256":"192def3c251db63eb75f63e353d402c34a775738e96436b6cde323eadc6c2e27"}}