{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BJMR7SEXARIPHKGXGYPWRUCE34","short_pith_number":"pith:BJMR7SEX","schema_version":"1.0","canonical_sha256":"0a591fc8970450f3a8d7361f68d044df15416a60f69171667c59561cd3133a2e","source":{"kind":"arxiv","id":"2305.15217","version":3},"attestation_state":"computed","paper":{"title":"L-CAD: Language-based Colorization with Any-level Descriptions using Diffusion Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Boxin Shi, Peixuan Zhang, Shuchen Weng, Si Li, Yu Li, Zheng Chang","submitted_at":"2023-05-24T14:57:42Z","abstract_excerpt":"Language-based colorization produces plausible and visually pleasing colors under the guidance of user-friendly natural language descriptions. Previous methods implicitly assume that users provide comprehensive color descriptions for most of the objects in the image, which leads to suboptimal performance. In this paper, we propose a unified model to perform language-based colorization with any-level descriptions. We leverage the pretrained cross-modality generative model for its robust language understanding and rich color priors to handle the inherent ambiguity of any-level descriptions. We f"},"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":"2305.15217","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-24T14:57:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dbc738c99fc862836e5891218ef0e910d782e0e22b494b20d1d7315f36c82272","abstract_canon_sha256":"10449c7a3ec7f505e2e40188e1fcdd8806ce49fc0b8525c638fcbd75bc93b900"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:03:26.050415Z","signature_b64":"0n/SA5YqQ7/ZGpomaiePEU1uw6JasH9ZHJ935xLLqLOGW2Vsp2FaWYRMXXkhxDLGHDJ3ibp43rOPN3/iL6KPDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a591fc8970450f3a8d7361f68d044df15416a60f69171667c59561cd3133a2e","last_reissued_at":"2026-07-05T07:03:26.049940Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:03:26.049940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"L-CAD: Language-based Colorization with Any-level Descriptions using Diffusion Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Boxin Shi, Peixuan Zhang, Shuchen Weng, Si Li, Yu Li, Zheng Chang","submitted_at":"2023-05-24T14:57:42Z","abstract_excerpt":"Language-based colorization produces plausible and visually pleasing colors under the guidance of user-friendly natural language descriptions. Previous methods implicitly assume that users provide comprehensive color descriptions for most of the objects in the image, which leads to suboptimal performance. In this paper, we propose a unified model to perform language-based colorization with any-level descriptions. We leverage the pretrained cross-modality generative model for its robust language understanding and rich color priors to handle the inherent ambiguity of any-level descriptions. We f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15217","kind":"arxiv","version":3},"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/2305.15217/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":"2305.15217","created_at":"2026-07-05T07:03:26.049994+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.15217v3","created_at":"2026-07-05T07:03:26.049994+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15217","created_at":"2026-07-05T07:03:26.049994+00:00"},{"alias_kind":"pith_short_12","alias_value":"BJMR7SEXARIP","created_at":"2026-07-05T07:03:26.049994+00:00"},{"alias_kind":"pith_short_16","alias_value":"BJMR7SEXARIPHKGX","created_at":"2026-07-05T07:03:26.049994+00:00"},{"alias_kind":"pith_short_8","alias_value":"BJMR7SEX","created_at":"2026-07-05T07:03:26.049994+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/BJMR7SEXARIPHKGXGYPWRUCE34","json":"https://pith.science/pith/BJMR7SEXARIPHKGXGYPWRUCE34.json","graph_json":"https://pith.science/api/pith-number/BJMR7SEXARIPHKGXGYPWRUCE34/graph.json","events_json":"https://pith.science/api/pith-number/BJMR7SEXARIPHKGXGYPWRUCE34/events.json","paper":"https://pith.science/paper/BJMR7SEX"},"agent_actions":{"view_html":"https://pith.science/pith/BJMR7SEXARIPHKGXGYPWRUCE34","download_json":"https://pith.science/pith/BJMR7SEXARIPHKGXGYPWRUCE34.json","view_paper":"https://pith.science/paper/BJMR7SEX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.15217&json=true","fetch_graph":"https://pith.science/api/pith-number/BJMR7SEXARIPHKGXGYPWRUCE34/graph.json","fetch_events":"https://pith.science/api/pith-number/BJMR7SEXARIPHKGXGYPWRUCE34/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BJMR7SEXARIPHKGXGYPWRUCE34/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BJMR7SEXARIPHKGXGYPWRUCE34/action/storage_attestation","attest_author":"https://pith.science/pith/BJMR7SEXARIPHKGXGYPWRUCE34/action/author_attestation","sign_citation":"https://pith.science/pith/BJMR7SEXARIPHKGXGYPWRUCE34/action/citation_signature","submit_replication":"https://pith.science/pith/BJMR7SEXARIPHKGXGYPWRUCE34/action/replication_record"}},"created_at":"2026-07-05T07:03:26.049994+00:00","updated_at":"2026-07-05T07:03:26.049994+00:00"}