{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QKAFACV4SUUIHGOZP673S2UOL2","short_pith_number":"pith:QKAFACV4","schema_version":"1.0","canonical_sha256":"8280500abc95288399d97fbfb96a8e5e819fe2649c2034e3598e14a342b55918","source":{"kind":"arxiv","id":"2508.09045","version":1},"attestation_state":"computed","paper":{"title":"Per-Query Visual Concept Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dvir Samuel, Gal Chechik, Ori Malca","submitted_at":"2025-08-12T16:07:27Z","abstract_excerpt":"Visual concept learning, also known as Text-to-image personalization, is the process of teaching new concepts to a pretrained model. This has numerous applications from product placement to entertainment and personalized design. Here we show that many existing methods can be substantially augmented by adding a personalization step that is (1) specific to the prompt and noise seed, and (2) using two loss terms based on the self- and cross- attention, capturing the identity of the personalized concept. Specifically, we leverage PDM features -- previously designed to capture identity -- and show "},"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":"2508.09045","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-12T16:07:27Z","cross_cats_sorted":[],"title_canon_sha256":"8c23711f7ef255de1fe95721f7e04b21bf8cccf66b9ded04e21655333137c922","abstract_canon_sha256":"8d66f8af0cdf2dea601004059e4c9aa18afc1819138b6db265493fa0b156d98b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:38.975029Z","signature_b64":"DEF6kO4BimJ3fAj7HsmNui3D2DN9ap/tZgN5T5o/haZN+R7G/5NxNLAcxqd73kQVcf7/4BcRKLk3ZdvG0W0ABg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8280500abc95288399d97fbfb96a8e5e819fe2649c2034e3598e14a342b55918","last_reissued_at":"2026-07-05T11:52:38.974497Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:38.974497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Per-Query Visual Concept Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dvir Samuel, Gal Chechik, Ori Malca","submitted_at":"2025-08-12T16:07:27Z","abstract_excerpt":"Visual concept learning, also known as Text-to-image personalization, is the process of teaching new concepts to a pretrained model. This has numerous applications from product placement to entertainment and personalized design. Here we show that many existing methods can be substantially augmented by adding a personalization step that is (1) specific to the prompt and noise seed, and (2) using two loss terms based on the self- and cross- attention, capturing the identity of the personalized concept. Specifically, we leverage PDM features -- previously designed to capture identity -- and show "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09045","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/2508.09045/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":"2508.09045","created_at":"2026-07-05T11:52:38.974571+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.09045v1","created_at":"2026-07-05T11:52:38.974571+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09045","created_at":"2026-07-05T11:52:38.974571+00:00"},{"alias_kind":"pith_short_12","alias_value":"QKAFACV4SUUI","created_at":"2026-07-05T11:52:38.974571+00:00"},{"alias_kind":"pith_short_16","alias_value":"QKAFACV4SUUIHGOZ","created_at":"2026-07-05T11:52:38.974571+00:00"},{"alias_kind":"pith_short_8","alias_value":"QKAFACV4","created_at":"2026-07-05T11:52:38.974571+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/QKAFACV4SUUIHGOZP673S2UOL2","json":"https://pith.science/pith/QKAFACV4SUUIHGOZP673S2UOL2.json","graph_json":"https://pith.science/api/pith-number/QKAFACV4SUUIHGOZP673S2UOL2/graph.json","events_json":"https://pith.science/api/pith-number/QKAFACV4SUUIHGOZP673S2UOL2/events.json","paper":"https://pith.science/paper/QKAFACV4"},"agent_actions":{"view_html":"https://pith.science/pith/QKAFACV4SUUIHGOZP673S2UOL2","download_json":"https://pith.science/pith/QKAFACV4SUUIHGOZP673S2UOL2.json","view_paper":"https://pith.science/paper/QKAFACV4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.09045&json=true","fetch_graph":"https://pith.science/api/pith-number/QKAFACV4SUUIHGOZP673S2UOL2/graph.json","fetch_events":"https://pith.science/api/pith-number/QKAFACV4SUUIHGOZP673S2UOL2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QKAFACV4SUUIHGOZP673S2UOL2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QKAFACV4SUUIHGOZP673S2UOL2/action/storage_attestation","attest_author":"https://pith.science/pith/QKAFACV4SUUIHGOZP673S2UOL2/action/author_attestation","sign_citation":"https://pith.science/pith/QKAFACV4SUUIHGOZP673S2UOL2/action/citation_signature","submit_replication":"https://pith.science/pith/QKAFACV4SUUIHGOZP673S2UOL2/action/replication_record"}},"created_at":"2026-07-05T11:52:38.974571+00:00","updated_at":"2026-07-05T11:52:38.974571+00:00"}