{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CTAXE6E6Q2COYMV6FKPCAKFYTD","short_pith_number":"pith:CTAXE6E6","schema_version":"1.0","canonical_sha256":"14c172789e8684ec32be2a9e2028b898e40e136d605c0af70443096e28b17902","source":{"kind":"arxiv","id":"2305.16311","version":2},"attestation_state":"computed","paper":{"title":"Break-A-Scene: Extracting Multiple Concepts from a Single Image","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Daniel Cohen-Or, Dani Lischinski, Kfir Aberman, Ohad Fried, Omri Avrahami","submitted_at":"2023-05-25T17:59:04Z","abstract_excerpt":"Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images with variations in backgrounds and poses, and struggle when adapted to a different scenario. In this work, we introduce the task of textual scene decomposition: given a single image of a scene that may contain several concepts, we aim to extract a distinct text token for each concept, enabling fine-grained control over the generated scenes. To this end, we pr"},"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":"2305.16311","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T17:59:04Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"c558555dab4efc35100842ad30ac9f460a41f34ab52da89a328c10440ae77297","abstract_canon_sha256":"4709a3742e35124cec3501d0b486b42adcd4c141d1ee58ba6efe94a8f043b06e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:26.388943Z","signature_b64":"N3FuAUf7ZToKGcczDHBx7ehRjIapx3ivi7ML56XSrivcrq62JPO7Qovpjk5Mjx32Kqzr73f0jTdYhDid7eyLBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14c172789e8684ec32be2a9e2028b898e40e136d605c0af70443096e28b17902","last_reissued_at":"2026-07-05T07:23:26.388497Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:26.388497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Break-A-Scene: Extracting Multiple Concepts from a Single Image","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Daniel Cohen-Or, Dani Lischinski, Kfir Aberman, Ohad Fried, Omri Avrahami","submitted_at":"2023-05-25T17:59:04Z","abstract_excerpt":"Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images with variations in backgrounds and poses, and struggle when adapted to a different scenario. In this work, we introduce the task of textual scene decomposition: given a single image of a scene that may contain several concepts, we aim to extract a distinct text token for each concept, enabling fine-grained control over the generated scenes. To this end, we pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16311","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/2305.16311/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":"2305.16311","created_at":"2026-07-05T07:23:26.388553+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.16311v2","created_at":"2026-07-05T07:23:26.388553+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16311","created_at":"2026-07-05T07:23:26.388553+00:00"},{"alias_kind":"pith_short_12","alias_value":"CTAXE6E6Q2CO","created_at":"2026-07-05T07:23:26.388553+00:00"},{"alias_kind":"pith_short_16","alias_value":"CTAXE6E6Q2COYMV6","created_at":"2026-07-05T07:23:26.388553+00:00"},{"alias_kind":"pith_short_8","alias_value":"CTAXE6E6","created_at":"2026-07-05T07:23:26.388553+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.17963","citing_title":"Zero-Shot Dynamic Concept Personalization with Grid-Based LoRA","ref_index":2023,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CTAXE6E6Q2COYMV6FKPCAKFYTD","json":"https://pith.science/pith/CTAXE6E6Q2COYMV6FKPCAKFYTD.json","graph_json":"https://pith.science/api/pith-number/CTAXE6E6Q2COYMV6FKPCAKFYTD/graph.json","events_json":"https://pith.science/api/pith-number/CTAXE6E6Q2COYMV6FKPCAKFYTD/events.json","paper":"https://pith.science/paper/CTAXE6E6"},"agent_actions":{"view_html":"https://pith.science/pith/CTAXE6E6Q2COYMV6FKPCAKFYTD","download_json":"https://pith.science/pith/CTAXE6E6Q2COYMV6FKPCAKFYTD.json","view_paper":"https://pith.science/paper/CTAXE6E6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.16311&json=true","fetch_graph":"https://pith.science/api/pith-number/CTAXE6E6Q2COYMV6FKPCAKFYTD/graph.json","fetch_events":"https://pith.science/api/pith-number/CTAXE6E6Q2COYMV6FKPCAKFYTD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CTAXE6E6Q2COYMV6FKPCAKFYTD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CTAXE6E6Q2COYMV6FKPCAKFYTD/action/storage_attestation","attest_author":"https://pith.science/pith/CTAXE6E6Q2COYMV6FKPCAKFYTD/action/author_attestation","sign_citation":"https://pith.science/pith/CTAXE6E6Q2COYMV6FKPCAKFYTD/action/citation_signature","submit_replication":"https://pith.science/pith/CTAXE6E6Q2COYMV6FKPCAKFYTD/action/replication_record"}},"created_at":"2026-07-05T07:23:26.388553+00:00","updated_at":"2026-07-05T07:23:26.388553+00:00"}