{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:6MTETVVFZKD5EJIFZ2KPAPBNUL","short_pith_number":"pith:6MTETVVF","schema_version":"1.0","canonical_sha256":"f32649d6a5ca87d22505ce94f03c2da2fd453bc99092ab37629cfbdeb94c3e44","source":{"kind":"arxiv","id":"1903.11029","version":1},"attestation_state":"computed","paper":{"title":"Optimising the Input Image to Improve Visual Relationship Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrian Muscat, Noel Mizzi","submitted_at":"2019-03-26T17:21:06Z","abstract_excerpt":"Visual Relationship Detection is defined as, given an image composed of a subject and an object, the correct relation is predicted. To improve the visual part of this difficult problem, ten preprocessing methods were tested to determine whether the widely used Union method yields the optimal results. Therefore, focusing solely on predicate prediction, no object detection and linguistic knowledge were used to prevent them from affecting the comparison results. Once fine-tuned, the Visual Geometry Group models were evaluated using Recall@1, per-predicate recall, activation maximisations, class a"},"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":"1903.11029","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-26T17:21:06Z","cross_cats_sorted":[],"title_canon_sha256":"ea0b79284f597018f9ebe91e8f3928cf2a896121928f66ed30363188b4c333d8","abstract_canon_sha256":"18f6540a82ce7b16ae4764988f8c875f9721b932cfbf239992a55d97625e8c3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:16.924894Z","signature_b64":"EUp17UgIxpNSji1Vc/9dwbOMdn9E5sj2WFZ3bJPvB8vDWR4DLfsWrIXFg9Jo4YDAV6FYTU4yORe9DHew4Wf2Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f32649d6a5ca87d22505ce94f03c2da2fd453bc99092ab37629cfbdeb94c3e44","last_reissued_at":"2026-05-17T23:50:16.924246Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:16.924246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimising the Input Image to Improve Visual Relationship Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrian Muscat, Noel Mizzi","submitted_at":"2019-03-26T17:21:06Z","abstract_excerpt":"Visual Relationship Detection is defined as, given an image composed of a subject and an object, the correct relation is predicted. To improve the visual part of this difficult problem, ten preprocessing methods were tested to determine whether the widely used Union method yields the optimal results. Therefore, focusing solely on predicate prediction, no object detection and linguistic knowledge were used to prevent them from affecting the comparison results. Once fine-tuned, the Visual Geometry Group models were evaluated using Recall@1, per-predicate recall, activation maximisations, class a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11029","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":"1903.11029","created_at":"2026-05-17T23:50:16.924341+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.11029v1","created_at":"2026-05-17T23:50:16.924341+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11029","created_at":"2026-05-17T23:50:16.924341+00:00"},{"alias_kind":"pith_short_12","alias_value":"6MTETVVFZKD5","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"6MTETVVFZKD5EJIF","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"6MTETVVF","created_at":"2026-05-18T12:33:10.108867+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/6MTETVVFZKD5EJIFZ2KPAPBNUL","json":"https://pith.science/pith/6MTETVVFZKD5EJIFZ2KPAPBNUL.json","graph_json":"https://pith.science/api/pith-number/6MTETVVFZKD5EJIFZ2KPAPBNUL/graph.json","events_json":"https://pith.science/api/pith-number/6MTETVVFZKD5EJIFZ2KPAPBNUL/events.json","paper":"https://pith.science/paper/6MTETVVF"},"agent_actions":{"view_html":"https://pith.science/pith/6MTETVVFZKD5EJIFZ2KPAPBNUL","download_json":"https://pith.science/pith/6MTETVVFZKD5EJIFZ2KPAPBNUL.json","view_paper":"https://pith.science/paper/6MTETVVF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.11029&json=true","fetch_graph":"https://pith.science/api/pith-number/6MTETVVFZKD5EJIFZ2KPAPBNUL/graph.json","fetch_events":"https://pith.science/api/pith-number/6MTETVVFZKD5EJIFZ2KPAPBNUL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6MTETVVFZKD5EJIFZ2KPAPBNUL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6MTETVVFZKD5EJIFZ2KPAPBNUL/action/storage_attestation","attest_author":"https://pith.science/pith/6MTETVVFZKD5EJIFZ2KPAPBNUL/action/author_attestation","sign_citation":"https://pith.science/pith/6MTETVVFZKD5EJIFZ2KPAPBNUL/action/citation_signature","submit_replication":"https://pith.science/pith/6MTETVVFZKD5EJIFZ2KPAPBNUL/action/replication_record"}},"created_at":"2026-05-17T23:50:16.924341+00:00","updated_at":"2026-05-17T23:50:16.924341+00:00"}