{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FKWYIAOMU6ZR3XKYQXRJAPYPAR","short_pith_number":"pith:FKWYIAOM","schema_version":"1.0","canonical_sha256":"2aad8401cca7b31ddd5885e2903f0f04534fc107ce9c9ecfe8537b6a5058c43a","source":{"kind":"arxiv","id":"2303.15166","version":1},"attestation_state":"computed","paper":{"title":"Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoyuan Tian, Paul L. Rosin, Ran Yi, Yu-Kun Lai, Zhihao Gu","submitted_at":"2023-03-27T12:59:15Z","abstract_excerpt":"Image aesthetics assessment (IAA) is a challenging task due to its highly subjective nature. Most of the current studies rely on large-scale datasets (e.g., AVA and AADB) to learn a general model for all kinds of photography images. However, little light has been shed on measuring the aesthetic quality of artistic images, and the existing datasets only contain relatively few artworks. Such a defect is a great obstacle to the aesthetic assessment of artistic images. To fill the gap in the field of artistic image aesthetics assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbr"},"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":"2303.15166","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-27T12:59:15Z","cross_cats_sorted":[],"title_canon_sha256":"248ce1d957661adb6fcd12342fb83619f09535f80cd702529cb376d27168314a","abstract_canon_sha256":"4d78521ce440570df9f5db59dc40b5b088954f8321aaf251cc51b16cbddee546"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:55:01.835502Z","signature_b64":"4BSEkDgrU3vY9CgA6S/n8pFHH7yxAHT9mgXIG6cgheCpeJNo+dxHfU8OfFgff8dLkUgrDKZwFD4RBae/UCyUBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2aad8401cca7b31ddd5885e2903f0f04534fc107ce9c9ecfe8537b6a5058c43a","last_reissued_at":"2026-07-05T05:55:01.835007Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:55:01.835007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoyuan Tian, Paul L. Rosin, Ran Yi, Yu-Kun Lai, Zhihao Gu","submitted_at":"2023-03-27T12:59:15Z","abstract_excerpt":"Image aesthetics assessment (IAA) is a challenging task due to its highly subjective nature. Most of the current studies rely on large-scale datasets (e.g., AVA and AADB) to learn a general model for all kinds of photography images. However, little light has been shed on measuring the aesthetic quality of artistic images, and the existing datasets only contain relatively few artworks. Such a defect is a great obstacle to the aesthetic assessment of artistic images. To fill the gap in the field of artistic image aesthetics assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.15166","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/2303.15166/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":"2303.15166","created_at":"2026-07-05T05:55:01.835066+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.15166v1","created_at":"2026-07-05T05:55:01.835066+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.15166","created_at":"2026-07-05T05:55:01.835066+00:00"},{"alias_kind":"pith_short_12","alias_value":"FKWYIAOMU6ZR","created_at":"2026-07-05T05:55:01.835066+00:00"},{"alias_kind":"pith_short_16","alias_value":"FKWYIAOMU6ZR3XKY","created_at":"2026-07-05T05:55:01.835066+00:00"},{"alias_kind":"pith_short_8","alias_value":"FKWYIAOM","created_at":"2026-07-05T05:55:01.835066+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":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FKWYIAOMU6ZR3XKYQXRJAPYPAR","json":"https://pith.science/pith/FKWYIAOMU6ZR3XKYQXRJAPYPAR.json","graph_json":"https://pith.science/api/pith-number/FKWYIAOMU6ZR3XKYQXRJAPYPAR/graph.json","events_json":"https://pith.science/api/pith-number/FKWYIAOMU6ZR3XKYQXRJAPYPAR/events.json","paper":"https://pith.science/paper/FKWYIAOM"},"agent_actions":{"view_html":"https://pith.science/pith/FKWYIAOMU6ZR3XKYQXRJAPYPAR","download_json":"https://pith.science/pith/FKWYIAOMU6ZR3XKYQXRJAPYPAR.json","view_paper":"https://pith.science/paper/FKWYIAOM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.15166&json=true","fetch_graph":"https://pith.science/api/pith-number/FKWYIAOMU6ZR3XKYQXRJAPYPAR/graph.json","fetch_events":"https://pith.science/api/pith-number/FKWYIAOMU6ZR3XKYQXRJAPYPAR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FKWYIAOMU6ZR3XKYQXRJAPYPAR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FKWYIAOMU6ZR3XKYQXRJAPYPAR/action/storage_attestation","attest_author":"https://pith.science/pith/FKWYIAOMU6ZR3XKYQXRJAPYPAR/action/author_attestation","sign_citation":"https://pith.science/pith/FKWYIAOMU6ZR3XKYQXRJAPYPAR/action/citation_signature","submit_replication":"https://pith.science/pith/FKWYIAOMU6ZR3XKYQXRJAPYPAR/action/replication_record"}},"created_at":"2026-07-05T05:55:01.835066+00:00","updated_at":"2026-07-05T05:55:01.835066+00:00"}