{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:WCGRNEKMHK5ZJZQ5EIFNIQNBM3","short_pith_number":"pith:WCGRNEKM","canonical_record":{"source":{"id":"2309.06809","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-13T08:59:54Z","cross_cats_sorted":[],"title_canon_sha256":"631312801e29b6a195304e7c0af762bb2602f22d21c64597eede85934de9dab9","abstract_canon_sha256":"5881bf83ae2a80818758fbeb5b315ba4b38177888aea60db147bab1f54a577da"},"schema_version":"1.0"},"canonical_sha256":"b08d16914c3abb94e61d220ad441a166ee1281a0eecbf77bbae03da3f046ae58","source":{"kind":"arxiv","id":"2309.06809","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.06809","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"arxiv_version","alias_value":"2309.06809v1","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.06809","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"pith_short_12","alias_value":"WCGRNEKMHK5Z","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"pith_short_16","alias_value":"WCGRNEKMHK5ZJZQ5","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"pith_short_8","alias_value":"WCGRNEKM","created_at":"2026-07-05T06:50:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:WCGRNEKMHK5ZJZQ5EIFNIQNBM3","target":"record","payload":{"canonical_record":{"source":{"id":"2309.06809","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-13T08:59:54Z","cross_cats_sorted":[],"title_canon_sha256":"631312801e29b6a195304e7c0af762bb2602f22d21c64597eede85934de9dab9","abstract_canon_sha256":"5881bf83ae2a80818758fbeb5b315ba4b38177888aea60db147bab1f54a577da"},"schema_version":"1.0"},"canonical_sha256":"b08d16914c3abb94e61d220ad441a166ee1281a0eecbf77bbae03da3f046ae58","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:50:22.810353Z","signature_b64":"c5dwpdeRBmrnQbsyupaId8HDNvfm4Y9hlmqjuCC+UxO2Y7NPQGFwK21GtPXdodMdpG3DzLqfVh+Soo9DxfmhBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b08d16914c3abb94e61d220ad441a166ee1281a0eecbf77bbae03da3f046ae58","last_reissued_at":"2026-07-05T06:50:22.809852Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:50:22.809852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.06809","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:50:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xadCgCoKL1R8ht2fAfrhGUfik0gOyzO81iDxBLV+yzQccp4aaG7jV43xro6m0jwVH+6YzqNxnkZhLkNvU23dDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T21:16:30.087529Z"},"content_sha256":"e09bc38d19c0add56664f82fd95791eb19532dbccd06da0e8ec250a0017f02b8","schema_version":"1.0","event_id":"sha256:e09bc38d19c0add56664f82fd95791eb19532dbccd06da0e8ec250a0017f02b8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:WCGRNEKMHK5ZJZQ5EIFNIQNBM3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Horst Bischof, Horst Possegger, Leonid Karlinsky, M. Jehanzeb Mirza, Rogerio Feris, Wei Lin","submitted_at":"2023-09-13T08:59:54Z","abstract_excerpt":"Vision and Language Models (VLMs), such as CLIP, have enabled visual recognition of a potentially unlimited set of categories described by text prompts. However, for the best visual recognition performance, these models still require tuning to better fit the data distributions of the downstream tasks, in order to overcome the domain shift from the web-based pre-training data. Recently, it has been shown that it is possible to effectively tune VLMs without any paired data, and in particular to effectively improve VLMs visual recognition performance using text-only training data generated by Lar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.06809","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/2309.06809/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":"2026-07-05T06:50:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IDOmReEleVBHA5rPSlRW6TDYx2QIRNOiqbXENB2CXzCj/IXIECB6vyOV49oRqnYpOj7BRXcRjA9JfA1X1mObBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T21:16:30.088110Z"},"content_sha256":"7a222b7031d6d3fbde54a722e3430d8386721d686e780e6c589af872334a710a","schema_version":"1.0","event_id":"sha256:7a222b7031d6d3fbde54a722e3430d8386721d686e780e6c589af872334a710a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WCGRNEKMHK5ZJZQ5EIFNIQNBM3/bundle.json","state_url":"https://pith.science/pith/WCGRNEKMHK5ZJZQ5EIFNIQNBM3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WCGRNEKMHK5ZJZQ5EIFNIQNBM3/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":"2026-08-19T21:16:30Z","links":{"resolver":"https://pith.science/pith/WCGRNEKMHK5ZJZQ5EIFNIQNBM3","bundle":"https://pith.science/pith/WCGRNEKMHK5ZJZQ5EIFNIQNBM3/bundle.json","state":"https://pith.science/pith/WCGRNEKMHK5ZJZQ5EIFNIQNBM3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WCGRNEKMHK5ZJZQ5EIFNIQNBM3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WCGRNEKMHK5ZJZQ5EIFNIQNBM3","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":"5881bf83ae2a80818758fbeb5b315ba4b38177888aea60db147bab1f54a577da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-13T08:59:54Z","title_canon_sha256":"631312801e29b6a195304e7c0af762bb2602f22d21c64597eede85934de9dab9"},"schema_version":"1.0","source":{"id":"2309.06809","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.06809","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"arxiv_version","alias_value":"2309.06809v1","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.06809","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"pith_short_12","alias_value":"WCGRNEKMHK5Z","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"pith_short_16","alias_value":"WCGRNEKMHK5ZJZQ5","created_at":"2026-07-05T06:50:22Z"},{"alias_kind":"pith_short_8","alias_value":"WCGRNEKM","created_at":"2026-07-05T06:50:22Z"}],"graph_snapshots":[{"event_id":"sha256:7a222b7031d6d3fbde54a722e3430d8386721d686e780e6c589af872334a710a","target":"graph","created_at":"2026-07-05T06:50:22Z","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/2309.06809/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Vision and Language Models (VLMs), such as CLIP, have enabled visual recognition of a potentially unlimited set of categories described by text prompts. However, for the best visual recognition performance, these models still require tuning to better fit the data distributions of the downstream tasks, in order to overcome the domain shift from the web-based pre-training data. Recently, it has been shown that it is possible to effectively tune VLMs without any paired data, and in particular to effectively improve VLMs visual recognition performance using text-only training data generated by Lar","authors_text":"Horst Bischof, Horst Possegger, Leonid Karlinsky, M. Jehanzeb Mirza, Rogerio Feris, Wei Lin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-13T08:59:54Z","title":"TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.06809","kind":"arxiv","version":1},"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:e09bc38d19c0add56664f82fd95791eb19532dbccd06da0e8ec250a0017f02b8","target":"record","created_at":"2026-07-05T06:50:22Z","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":"5881bf83ae2a80818758fbeb5b315ba4b38177888aea60db147bab1f54a577da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-13T08:59:54Z","title_canon_sha256":"631312801e29b6a195304e7c0af762bb2602f22d21c64597eede85934de9dab9"},"schema_version":"1.0","source":{"id":"2309.06809","kind":"arxiv","version":1}},"canonical_sha256":"b08d16914c3abb94e61d220ad441a166ee1281a0eecbf77bbae03da3f046ae58","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b08d16914c3abb94e61d220ad441a166ee1281a0eecbf77bbae03da3f046ae58","first_computed_at":"2026-07-05T06:50:22.809852Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:50:22.809852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"c5dwpdeRBmrnQbsyupaId8HDNvfm4Y9hlmqjuCC+UxO2Y7NPQGFwK21GtPXdodMdpG3DzLqfVh+Soo9DxfmhBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:50:22.810353Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.06809","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e09bc38d19c0add56664f82fd95791eb19532dbccd06da0e8ec250a0017f02b8","sha256:7a222b7031d6d3fbde54a722e3430d8386721d686e780e6c589af872334a710a"],"state_sha256":"5200ed143a6de91173ba830e088840cca7adecb026730ad2336fbef120afbb50"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PC+UixzuJQzKNaVkeFtxgmktJSrKav06ZRGPMo7wU0FRfYKlKmRFp6wIoHH6LYUuVA90gsQYWbA288cpcdGiDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T21:16:30.096749Z","bundle_sha256":"744e52a9b4eb2cdc56a0f8690d533a99446d14c9147d2d25f6bbc0068bb4305e"}}