{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:CAMU3667423ANI26CGO2FHBHZK","short_pith_number":"pith:CAMU3667","canonical_record":{"source":{"id":"2302.13848","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-27T14:49:53Z","cross_cats_sorted":[],"title_canon_sha256":"335b77bebfabe18cc25fc382897b940870f2046a1cddaa625d1063be5abd1b28","abstract_canon_sha256":"695a274ad80ce96d812cdbbae46b84350eeb69cf149ff38353ffe713e8067878"},"schema_version":"1.0"},"canonical_sha256":"10194dfbdfe6b606a35e119da29c27caaa7d8c73893b802efc23534705118030","source":{"kind":"arxiv","id":"2302.13848","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.13848","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"arxiv_version","alias_value":"2302.13848v2","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.13848","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"pith_short_12","alias_value":"CAMU3667423A","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"pith_short_16","alias_value":"CAMU3667423ANI26","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"pith_short_8","alias_value":"CAMU3667","created_at":"2026-07-05T06:42:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:CAMU3667423ANI26CGO2FHBHZK","target":"record","payload":{"canonical_record":{"source":{"id":"2302.13848","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-27T14:49:53Z","cross_cats_sorted":[],"title_canon_sha256":"335b77bebfabe18cc25fc382897b940870f2046a1cddaa625d1063be5abd1b28","abstract_canon_sha256":"695a274ad80ce96d812cdbbae46b84350eeb69cf149ff38353ffe713e8067878"},"schema_version":"1.0"},"canonical_sha256":"10194dfbdfe6b606a35e119da29c27caaa7d8c73893b802efc23534705118030","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:42:19.809934Z","signature_b64":"YbpuDBIOOegKOqaPQDARS1K1elNZR/+Lz7coOES11WkcTapVSMtZL/52g+aQQQXbSN+2wdVy7AuGAk8TNmYjBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10194dfbdfe6b606a35e119da29c27caaa7d8c73893b802efc23534705118030","last_reissued_at":"2026-07-05T06:42:19.809466Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:42:19.809466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.13848","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:42:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ajfkKaKp15KrPPF9gabmYZ4BwYOzLMFTXtqyfx4AUAgM9Tpa9uyGIVIFx+p5hZwT224uf5uc9gXFzCHN1Uz7Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:49:29.959861Z"},"content_sha256":"0c626825547b063c57f8b4fe86735fc20d700b29098e4cb2d380666050d303cf","schema_version":"1.0","event_id":"sha256:0c626825547b063c57f8b4fe86735fc20d700b29098e4cb2d380666050d303cf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:CAMU3667423ANI26CGO2FHBHZK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jinfeng Bai, Lei Zhang, Wangmeng Zuo, Yabo Zhang, Yuxiang Wei, Zhilong Ji","submitted_at":"2023-02-27T14:49:53Z","abstract_excerpt":"In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation. Existing works generally learn such concepts in an optimization-based manner, yet bringing excessive computation or memory burden. In this paper, we instead propose a learning-based encoder, which consists of a global and a local mapping networks for fast and accurate customized text-to-image generation. In specific, the global mapping network projects the hierarchical features of a given image into multiple new words in the textual word embed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.13848","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/2302.13848/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:42:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"06WJXgcHlQETRePG/r9UI1WYoGDVEN8ZvAkcQFT+UU2W2ZVbNDrbXe5CM93V6xr/Nm2gDE3+xmPZuhlV/uRnCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:49:29.960358Z"},"content_sha256":"9d18e093a809c7f0aa9e30de7112a56fb3130fc27153010b8206c3cae6720632","schema_version":"1.0","event_id":"sha256:9d18e093a809c7f0aa9e30de7112a56fb3130fc27153010b8206c3cae6720632"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CAMU3667423ANI26CGO2FHBHZK/bundle.json","state_url":"https://pith.science/pith/CAMU3667423ANI26CGO2FHBHZK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CAMU3667423ANI26CGO2FHBHZK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T15:49:29Z","links":{"resolver":"https://pith.science/pith/CAMU3667423ANI26CGO2FHBHZK","bundle":"https://pith.science/pith/CAMU3667423ANI26CGO2FHBHZK/bundle.json","state":"https://pith.science/pith/CAMU3667423ANI26CGO2FHBHZK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CAMU3667423ANI26CGO2FHBHZK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:CAMU3667423ANI26CGO2FHBHZK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"695a274ad80ce96d812cdbbae46b84350eeb69cf149ff38353ffe713e8067878","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-27T14:49:53Z","title_canon_sha256":"335b77bebfabe18cc25fc382897b940870f2046a1cddaa625d1063be5abd1b28"},"schema_version":"1.0","source":{"id":"2302.13848","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.13848","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"arxiv_version","alias_value":"2302.13848v2","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.13848","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"pith_short_12","alias_value":"CAMU3667423A","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"pith_short_16","alias_value":"CAMU3667423ANI26","created_at":"2026-07-05T06:42:19Z"},{"alias_kind":"pith_short_8","alias_value":"CAMU3667","created_at":"2026-07-05T06:42:19Z"}],"graph_snapshots":[{"event_id":"sha256:9d18e093a809c7f0aa9e30de7112a56fb3130fc27153010b8206c3cae6720632","target":"graph","created_at":"2026-07-05T06:42:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2302.13848/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation. Existing works generally learn such concepts in an optimization-based manner, yet bringing excessive computation or memory burden. In this paper, we instead propose a learning-based encoder, which consists of a global and a local mapping networks for fast and accurate customized text-to-image generation. In specific, the global mapping network projects the hierarchical features of a given image into multiple new words in the textual word embed","authors_text":"Jinfeng Bai, Lei Zhang, Wangmeng Zuo, Yabo Zhang, Yuxiang Wei, Zhilong Ji","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-27T14:49:53Z","title":"ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.13848","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0c626825547b063c57f8b4fe86735fc20d700b29098e4cb2d380666050d303cf","target":"record","created_at":"2026-07-05T06:42:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"695a274ad80ce96d812cdbbae46b84350eeb69cf149ff38353ffe713e8067878","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-27T14:49:53Z","title_canon_sha256":"335b77bebfabe18cc25fc382897b940870f2046a1cddaa625d1063be5abd1b28"},"schema_version":"1.0","source":{"id":"2302.13848","kind":"arxiv","version":2}},"canonical_sha256":"10194dfbdfe6b606a35e119da29c27caaa7d8c73893b802efc23534705118030","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"10194dfbdfe6b606a35e119da29c27caaa7d8c73893b802efc23534705118030","first_computed_at":"2026-07-05T06:42:19.809466Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:42:19.809466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YbpuDBIOOegKOqaPQDARS1K1elNZR/+Lz7coOES11WkcTapVSMtZL/52g+aQQQXbSN+2wdVy7AuGAk8TNmYjBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:42:19.809934Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.13848","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c626825547b063c57f8b4fe86735fc20d700b29098e4cb2d380666050d303cf","sha256:9d18e093a809c7f0aa9e30de7112a56fb3130fc27153010b8206c3cae6720632"],"state_sha256":"9d7084d775622a0cbdfa2d052b61cd55c9971f8ab5b317ffdd794889791462d2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w2XgNJWfn+RI3kOGqyHTNTc2riFSfQOgUQmq22l+VmDD7OcA8EDIE6JJVcXYb44NoqEWYPxjXhRSmHoVj4jNCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T15:49:29.965487Z","bundle_sha256":"3abe264b3c062e224d2602a99da1c9979949ab70a01550b5567d1634f787c8f7"}}