{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WTHLPMRTZU4KYIIBBIMWLEGUYR","short_pith_number":"pith:WTHLPMRT","schema_version":"1.0","canonical_sha256":"b4ceb7b233cd38ac21010a196590d4c47f1a0b14b2ac11f05f74564b85eaa9cc","source":{"kind":"arxiv","id":"2407.07176","version":2},"attestation_state":"computed","paper":{"title":"Scaling Up Personalized Image Aesthetic Assessment via Task Vector Customization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jaegul Choo, Jooyeol Yun","submitted_at":"2024-07-09T18:42:41Z","abstract_excerpt":"The task of personalized image aesthetic assessment seeks to tailor aesthetic score prediction models to match individual preferences with just a few user-provided inputs. However, the scalability and generalization capabilities of current approaches are considerably restricted by their reliance on an expensive curated database. To overcome this long-standing scalability challenge, we present a unique approach that leverages readily available databases for general image aesthetic assessment and image quality assessment. Specifically, we view each database as a distinct image score regression t"},"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":"2407.07176","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-09T18:42:41Z","cross_cats_sorted":[],"title_canon_sha256":"9a51b8285a43971fac30c1077b924e8009d8c48cd8aabf1347fe6c8e4d97b040","abstract_canon_sha256":"11ca9ab02df89fb4d0ff34ec51c41ded1de09ce07aef22d1016677eda9c55b9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:21:06.380178Z","signature_b64":"bK+QeooCRC4c963SjC1dEnuMxkSqMeVzTvD9/rQmM2RjM+B1zu4/xhLtGLsvMNn+aoff04tlr7U9occLSpsoDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4ceb7b233cd38ac21010a196590d4c47f1a0b14b2ac11f05f74564b85eaa9cc","last_reissued_at":"2026-07-05T09:21:06.379013Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:21:06.379013Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scaling Up Personalized Image Aesthetic Assessment via Task Vector Customization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jaegul Choo, Jooyeol Yun","submitted_at":"2024-07-09T18:42:41Z","abstract_excerpt":"The task of personalized image aesthetic assessment seeks to tailor aesthetic score prediction models to match individual preferences with just a few user-provided inputs. However, the scalability and generalization capabilities of current approaches are considerably restricted by their reliance on an expensive curated database. To overcome this long-standing scalability challenge, we present a unique approach that leverages readily available databases for general image aesthetic assessment and image quality assessment. Specifically, we view each database as a distinct image score regression t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.07176","kind":"arxiv","version":2},"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/2407.07176/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":"2407.07176","created_at":"2026-07-05T09:21:06.379106+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.07176v2","created_at":"2026-07-05T09:21:06.379106+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.07176","created_at":"2026-07-05T09:21:06.379106+00:00"},{"alias_kind":"pith_short_12","alias_value":"WTHLPMRTZU4K","created_at":"2026-07-05T09:21:06.379106+00:00"},{"alias_kind":"pith_short_16","alias_value":"WTHLPMRTZU4KYIIB","created_at":"2026-07-05T09:21:06.379106+00:00"},{"alias_kind":"pith_short_8","alias_value":"WTHLPMRT","created_at":"2026-07-05T09:21:06.379106+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.14533","citing_title":"ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding","ref_index":2024,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WTHLPMRTZU4KYIIBBIMWLEGUYR","json":"https://pith.science/pith/WTHLPMRTZU4KYIIBBIMWLEGUYR.json","graph_json":"https://pith.science/api/pith-number/WTHLPMRTZU4KYIIBBIMWLEGUYR/graph.json","events_json":"https://pith.science/api/pith-number/WTHLPMRTZU4KYIIBBIMWLEGUYR/events.json","paper":"https://pith.science/paper/WTHLPMRT"},"agent_actions":{"view_html":"https://pith.science/pith/WTHLPMRTZU4KYIIBBIMWLEGUYR","download_json":"https://pith.science/pith/WTHLPMRTZU4KYIIBBIMWLEGUYR.json","view_paper":"https://pith.science/paper/WTHLPMRT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.07176&json=true","fetch_graph":"https://pith.science/api/pith-number/WTHLPMRTZU4KYIIBBIMWLEGUYR/graph.json","fetch_events":"https://pith.science/api/pith-number/WTHLPMRTZU4KYIIBBIMWLEGUYR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WTHLPMRTZU4KYIIBBIMWLEGUYR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WTHLPMRTZU4KYIIBBIMWLEGUYR/action/storage_attestation","attest_author":"https://pith.science/pith/WTHLPMRTZU4KYIIBBIMWLEGUYR/action/author_attestation","sign_citation":"https://pith.science/pith/WTHLPMRTZU4KYIIBBIMWLEGUYR/action/citation_signature","submit_replication":"https://pith.science/pith/WTHLPMRTZU4KYIIBBIMWLEGUYR/action/replication_record"}},"created_at":"2026-07-05T09:21:06.379106+00:00","updated_at":"2026-07-05T09:21:06.379106+00:00"}