{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EG6DLKN3XLFA3UF22QJS57RSGP","short_pith_number":"pith:EG6DLKN3","schema_version":"1.0","canonical_sha256":"21bc35a9bbbaca0dd0bad4132efe3233f9de3937000b796270ff1d8246fb7cea","source":{"kind":"arxiv","id":"2303.07096","version":1},"attestation_state":"computed","paper":{"title":"Prototype-based Embedding Network for Scene Graph Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Dai, Chaofan Zheng, Jingkuan Song, Lianli Gao, Xinyu Lyu","submitted_at":"2023-03-13T13:30:59Z","abstract_excerpt":"Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. However, due to the diverse visual appearance of numerous possible subject-object combinations, there is a large intra-class variation within each predicate category, e.g., \"man-eating-pizza, giraffe-eating-leaf\", and the severe inter-class similarity between different classes, e.g., \"man-holding-plate, man-eating-pizza\", in model's latent space. The above challenges prevent current SGG methods from acquiring robust features for reliable relation prediction. In this paper, w"},"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":"2303.07096","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-13T13:30:59Z","cross_cats_sorted":[],"title_canon_sha256":"34fcdaad4d31b2980fc2f497c5ec69d90a4fb9eb252a486026e935dde93017a8","abstract_canon_sha256":"d81c3af0c0e4129a0a1555432020368bff84ba938eead6f16d4b59d812f114ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:50:26.524975Z","signature_b64":"UAjObtUzUaIOLgpceWYZbBoks2FQarX5ywgGTYp9BCdPAeTq3V9MFD9vR1iQeMnNmcBNSFqETc7o5bjHyN+KDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21bc35a9bbbaca0dd0bad4132efe3233f9de3937000b796270ff1d8246fb7cea","last_reissued_at":"2026-07-05T05:50:26.524554Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:50:26.524554Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prototype-based Embedding Network for Scene Graph Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Dai, Chaofan Zheng, Jingkuan Song, Lianli Gao, Xinyu Lyu","submitted_at":"2023-03-13T13:30:59Z","abstract_excerpt":"Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. However, due to the diverse visual appearance of numerous possible subject-object combinations, there is a large intra-class variation within each predicate category, e.g., \"man-eating-pizza, giraffe-eating-leaf\", and the severe inter-class similarity between different classes, e.g., \"man-holding-plate, man-eating-pizza\", in model's latent space. The above challenges prevent current SGG methods from acquiring robust features for reliable relation prediction. In this paper, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.07096","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/2303.07096/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":"2303.07096","created_at":"2026-07-05T05:50:26.524610+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.07096v1","created_at":"2026-07-05T05:50:26.524610+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.07096","created_at":"2026-07-05T05:50:26.524610+00:00"},{"alias_kind":"pith_short_12","alias_value":"EG6DLKN3XLFA","created_at":"2026-07-05T05:50:26.524610+00:00"},{"alias_kind":"pith_short_16","alias_value":"EG6DLKN3XLFA3UF2","created_at":"2026-07-05T05:50:26.524610+00:00"},{"alias_kind":"pith_short_8","alias_value":"EG6DLKN3","created_at":"2026-07-05T05:50:26.524610+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/EG6DLKN3XLFA3UF22QJS57RSGP","json":"https://pith.science/pith/EG6DLKN3XLFA3UF22QJS57RSGP.json","graph_json":"https://pith.science/api/pith-number/EG6DLKN3XLFA3UF22QJS57RSGP/graph.json","events_json":"https://pith.science/api/pith-number/EG6DLKN3XLFA3UF22QJS57RSGP/events.json","paper":"https://pith.science/paper/EG6DLKN3"},"agent_actions":{"view_html":"https://pith.science/pith/EG6DLKN3XLFA3UF22QJS57RSGP","download_json":"https://pith.science/pith/EG6DLKN3XLFA3UF22QJS57RSGP.json","view_paper":"https://pith.science/paper/EG6DLKN3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.07096&json=true","fetch_graph":"https://pith.science/api/pith-number/EG6DLKN3XLFA3UF22QJS57RSGP/graph.json","fetch_events":"https://pith.science/api/pith-number/EG6DLKN3XLFA3UF22QJS57RSGP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EG6DLKN3XLFA3UF22QJS57RSGP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EG6DLKN3XLFA3UF22QJS57RSGP/action/storage_attestation","attest_author":"https://pith.science/pith/EG6DLKN3XLFA3UF22QJS57RSGP/action/author_attestation","sign_citation":"https://pith.science/pith/EG6DLKN3XLFA3UF22QJS57RSGP/action/citation_signature","submit_replication":"https://pith.science/pith/EG6DLKN3XLFA3UF22QJS57RSGP/action/replication_record"}},"created_at":"2026-07-05T05:50:26.524610+00:00","updated_at":"2026-07-05T05:50:26.524610+00:00"}