{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IJVIFP6LPKS3M66PNXDUKSHWMH","short_pith_number":"pith:IJVIFP6L","schema_version":"1.0","canonical_sha256":"426a82bfcb7aa5b67bcf6dc74548f661c453cf8c0fda892706bee2716ea4c36f","source":{"kind":"arxiv","id":"1810.09162","version":1},"attestation_state":"computed","paper":{"title":"Exploring Correlations in Multiple Facial Attributes through Graph Attention Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li Sun, Yan Zhang","submitted_at":"2018-10-22T10:09:00Z","abstract_excerpt":"Estimating multiple attributes from a single facial image gives comprehensive descriptions on the high level semantics of the face. It is naturally regarded as a multi-task supervised learning problem with a single deep CNN, in which lower layers are shared, and higher ones are task-dependent with the multi-branch structure. Within the traditional deep multi-task learning (DMTL) framework, this paper intends to fully exploit the correlations among different attributes by constructing a graph. The node in graph represents the feature vector from a particular branch for a given attribute, and th"},"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":"1810.09162","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-22T10:09:00Z","cross_cats_sorted":[],"title_canon_sha256":"c9eca018eadd1494b380d5ef3b8cd3932af5429394c29acd50795329bb07d32f","abstract_canon_sha256":"b637873cac6e17b98d533ec33b5c773171f957f11b2e48d22f9f9e70ef4d3ccc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:40.904202Z","signature_b64":"35Bs53+xugQTMVzOilWn/MD7XI+OvqtYZ7vWTIjGYVF9ughX0/NETXF6D5vE4yjAvYYg4XwFwZgT6XYdS0p/BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"426a82bfcb7aa5b67bcf6dc74548f661c453cf8c0fda892706bee2716ea4c36f","last_reissued_at":"2026-05-18T00:02:40.903792Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:40.903792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Correlations in Multiple Facial Attributes through Graph Attention Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li Sun, Yan Zhang","submitted_at":"2018-10-22T10:09:00Z","abstract_excerpt":"Estimating multiple attributes from a single facial image gives comprehensive descriptions on the high level semantics of the face. It is naturally regarded as a multi-task supervised learning problem with a single deep CNN, in which lower layers are shared, and higher ones are task-dependent with the multi-branch structure. Within the traditional deep multi-task learning (DMTL) framework, this paper intends to fully exploit the correlations among different attributes by constructing a graph. The node in graph represents the feature vector from a particular branch for a given attribute, and th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09162","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":"1810.09162","created_at":"2026-05-18T00:02:40.903853+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.09162v1","created_at":"2026-05-18T00:02:40.903853+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09162","created_at":"2026-05-18T00:02:40.903853+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJVIFP6LPKS3","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJVIFP6LPKS3M66P","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJVIFP6L","created_at":"2026-05-18T12:32:31.084164+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/IJVIFP6LPKS3M66PNXDUKSHWMH","json":"https://pith.science/pith/IJVIFP6LPKS3M66PNXDUKSHWMH.json","graph_json":"https://pith.science/api/pith-number/IJVIFP6LPKS3M66PNXDUKSHWMH/graph.json","events_json":"https://pith.science/api/pith-number/IJVIFP6LPKS3M66PNXDUKSHWMH/events.json","paper":"https://pith.science/paper/IJVIFP6L"},"agent_actions":{"view_html":"https://pith.science/pith/IJVIFP6LPKS3M66PNXDUKSHWMH","download_json":"https://pith.science/pith/IJVIFP6LPKS3M66PNXDUKSHWMH.json","view_paper":"https://pith.science/paper/IJVIFP6L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.09162&json=true","fetch_graph":"https://pith.science/api/pith-number/IJVIFP6LPKS3M66PNXDUKSHWMH/graph.json","fetch_events":"https://pith.science/api/pith-number/IJVIFP6LPKS3M66PNXDUKSHWMH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJVIFP6LPKS3M66PNXDUKSHWMH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJVIFP6LPKS3M66PNXDUKSHWMH/action/storage_attestation","attest_author":"https://pith.science/pith/IJVIFP6LPKS3M66PNXDUKSHWMH/action/author_attestation","sign_citation":"https://pith.science/pith/IJVIFP6LPKS3M66PNXDUKSHWMH/action/citation_signature","submit_replication":"https://pith.science/pith/IJVIFP6LPKS3M66PNXDUKSHWMH/action/replication_record"}},"created_at":"2026-05-18T00:02:40.903853+00:00","updated_at":"2026-05-18T00:02:40.903853+00:00"}