{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZA27ZAYE5PVOBXTSSSWGK22ZS4","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":"b0e487253c0a4579574db77558d56ffe55133df785b95c825248d9726b7144c6","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-13T17:50:24Z","title_canon_sha256":"89b06c8372c13544cfc1f5f9e05597925b2142d575c887ec161da4d45fded9f5"},"schema_version":"1.0","source":{"id":"2210.07225","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.07225","created_at":"2026-07-05T05:06:25Z"},{"alias_kind":"arxiv_version","alias_value":"2210.07225v1","created_at":"2026-07-05T05:06:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.07225","created_at":"2026-07-05T05:06:25Z"},{"alias_kind":"pith_short_12","alias_value":"ZA27ZAYE5PVO","created_at":"2026-07-05T05:06:25Z"},{"alias_kind":"pith_short_16","alias_value":"ZA27ZAYE5PVOBXTS","created_at":"2026-07-05T05:06:25Z"},{"alias_kind":"pith_short_8","alias_value":"ZA27ZAYE","created_at":"2026-07-05T05:06:25Z"}],"graph_snapshots":[{"event_id":"sha256:506ef9caaf4622993cc9a5d9025f1de392dc41cdb0c0250603e2ae7e3ab7d0db","target":"graph","created_at":"2026-07-05T05:06:25Z","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/2210.07225/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prompt tuning, a parameter- and data-efficient transfer learning paradigm that tunes only a small number of parameters in a model's input space, has become a trend in the vision community since the emergence of large vision-language models like CLIP. We present a systematic study on two representative prompt tuning methods, namely text prompt tuning and visual prompt tuning. A major finding is that none of the unimodal prompt tuning methods performs consistently well: text prompt tuning fails on data with high intra-class visual variances while visual prompt tuning cannot handle low inter-clas","authors_text":"Chen Change Loy, Chen Huang, Kaiyang Zhou, Wei Li, Yuhang Zang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-13T17:50:24Z","title":"Unified Vision and Language Prompt Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.07225","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:e6790766dc153e7ee30440f8a26853b8bf7b840025522950ca779b93367958ed","target":"record","created_at":"2026-07-05T05:06:25Z","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":"b0e487253c0a4579574db77558d56ffe55133df785b95c825248d9726b7144c6","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-13T17:50:24Z","title_canon_sha256":"89b06c8372c13544cfc1f5f9e05597925b2142d575c887ec161da4d45fded9f5"},"schema_version":"1.0","source":{"id":"2210.07225","kind":"arxiv","version":1}},"canonical_sha256":"c835fc8304ebeae0de7294ac656b599720e225c618a8c57bb8d18fc24291491a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c835fc8304ebeae0de7294ac656b599720e225c618a8c57bb8d18fc24291491a","first_computed_at":"2026-07-05T05:06:25.901911Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:06:25.901911Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kVbJ3p5nXrJsaYswW9LKPaNmLyr+q5WSYQZYzVqvd6e+cI58LEpVTlbnaX/HPZLUkC18uLtE9nboh9aMyPzXCw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:06:25.902418Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.07225","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e6790766dc153e7ee30440f8a26853b8bf7b840025522950ca779b93367958ed","sha256:506ef9caaf4622993cc9a5d9025f1de392dc41cdb0c0250603e2ae7e3ab7d0db"],"state_sha256":"187a0595dc9496ac74767a4765a9972274412e6cc5a5ecf9a7f303ffb69879e3"}