{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:L27N2X5ZCAAUYXEAZ2LQWWIU6N","short_pith_number":"pith:L27N2X5Z","canonical_record":{"source":{"id":"2209.00647","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-01T17:59:33Z","cross_cats_sorted":[],"title_canon_sha256":"4ea9a092ae5362654d5b39aa90e40da0991de5e44bc2fbab05f104a831375bb0","abstract_canon_sha256":"314f62a3976392509fe3000590e720d975c9d0853c2cd8b378b691cd94029c83"},"schema_version":"1.0"},"canonical_sha256":"5ebedd5fb910014c5c80ce970b5914f34f04a18cfe4470ef2153d6422c1b7532","source":{"kind":"arxiv","id":"2209.00647","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.00647","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"arxiv_version","alias_value":"2209.00647v1","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.00647","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"pith_short_12","alias_value":"L27N2X5ZCAAU","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"pith_short_16","alias_value":"L27N2X5ZCAAUYXEA","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"pith_short_8","alias_value":"L27N2X5Z","created_at":"2026-07-05T04:53:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:L27N2X5ZCAAUYXEAZ2LQWWIU6N","target":"record","payload":{"canonical_record":{"source":{"id":"2209.00647","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-01T17:59:33Z","cross_cats_sorted":[],"title_canon_sha256":"4ea9a092ae5362654d5b39aa90e40da0991de5e44bc2fbab05f104a831375bb0","abstract_canon_sha256":"314f62a3976392509fe3000590e720d975c9d0853c2cd8b378b691cd94029c83"},"schema_version":"1.0"},"canonical_sha256":"5ebedd5fb910014c5c80ce970b5914f34f04a18cfe4470ef2153d6422c1b7532","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:53:52.369646Z","signature_b64":"DjzYm7SRNtCA5C/5uacjmTO9vckE2JOkr1b54PiCMzYCXpUjsp02oCqBGT+sLkG7KRWujgGucKwW55RpSMD/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ebedd5fb910014c5c80ce970b5914f34f04a18cfe4470ef2153d6422c1b7532","last_reissued_at":"2026-07-05T04:53:52.369271Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:53:52.369271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.00647","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":"2026-07-05T04:53:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cyTUIrOg4yHyDJhTHhiDxnBfeRWckEkQwCZBjRFdWlXndfuM577LxiHlmncCkzBESiIwOEjVRlFjVp3az5kbDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T07:05:16.606362Z"},"content_sha256":"f3b56294946ebe0cb9517e6c1010fb378e220c40aaa1e6707494bf75ee850289","schema_version":"1.0","event_id":"sha256:f3b56294946ebe0cb9517e6c1010fb378e220c40aaa1e6707494bf75ee850289"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:L27N2X5ZCAAUYXEAZ2LQWWIU6N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Visual Prompting via Image Inpainting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexei A. Efros, Amir Bar, Amir Globerson, Trevor Darrell, Yossi Gandelsman","submitted_at":"2022-09-01T17:59:33Z","abstract_excerpt":"How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investigates visual prompting: given input-output image example(s) of a new task at test time and a new input image, the goal is to automatically produce the output image, consistent with the given examples. We show that posing this problem as simple image inpainting - literally just filling in a hole in a concatenated visual prompt image - turns out to be surprisingly effective, provided that the inpainting algorithm has b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.00647","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/2209.00647/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":"2026-07-05T04:53:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SFyETPe77CX2WxmNjRLKsj9afUNc0T+It9OpA5cJbKXWMhCu/qAAR+IMcqvLfJ9NVlAQdp9FCwJxoFKwdmBCDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T07:05:16.606735Z"},"content_sha256":"8ad6eab730fbd2d530d004b626754c4157d3a04923e4f9c0dacc5bac6044696b","schema_version":"1.0","event_id":"sha256:8ad6eab730fbd2d530d004b626754c4157d3a04923e4f9c0dacc5bac6044696b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L27N2X5ZCAAUYXEAZ2LQWWIU6N/bundle.json","state_url":"https://pith.science/pith/L27N2X5ZCAAUYXEAZ2LQWWIU6N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L27N2X5ZCAAUYXEAZ2LQWWIU6N/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":"2026-08-14T07:05:16Z","links":{"resolver":"https://pith.science/pith/L27N2X5ZCAAUYXEAZ2LQWWIU6N","bundle":"https://pith.science/pith/L27N2X5ZCAAUYXEAZ2LQWWIU6N/bundle.json","state":"https://pith.science/pith/L27N2X5ZCAAUYXEAZ2LQWWIU6N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L27N2X5ZCAAUYXEAZ2LQWWIU6N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:L27N2X5ZCAAUYXEAZ2LQWWIU6N","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":"314f62a3976392509fe3000590e720d975c9d0853c2cd8b378b691cd94029c83","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-01T17:59:33Z","title_canon_sha256":"4ea9a092ae5362654d5b39aa90e40da0991de5e44bc2fbab05f104a831375bb0"},"schema_version":"1.0","source":{"id":"2209.00647","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.00647","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"arxiv_version","alias_value":"2209.00647v1","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.00647","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"pith_short_12","alias_value":"L27N2X5ZCAAU","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"pith_short_16","alias_value":"L27N2X5ZCAAUYXEA","created_at":"2026-07-05T04:53:52Z"},{"alias_kind":"pith_short_8","alias_value":"L27N2X5Z","created_at":"2026-07-05T04:53:52Z"}],"graph_snapshots":[{"event_id":"sha256:8ad6eab730fbd2d530d004b626754c4157d3a04923e4f9c0dacc5bac6044696b","target":"graph","created_at":"2026-07-05T04:53:52Z","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/2209.00647/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investigates visual prompting: given input-output image example(s) of a new task at test time and a new input image, the goal is to automatically produce the output image, consistent with the given examples. We show that posing this problem as simple image inpainting - literally just filling in a hole in a concatenated visual prompt image - turns out to be surprisingly effective, provided that the inpainting algorithm has b","authors_text":"Alexei A. Efros, Amir Bar, Amir Globerson, Trevor Darrell, Yossi Gandelsman","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-01T17:59:33Z","title":"Visual Prompting via Image Inpainting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.00647","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:f3b56294946ebe0cb9517e6c1010fb378e220c40aaa1e6707494bf75ee850289","target":"record","created_at":"2026-07-05T04:53:52Z","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":"314f62a3976392509fe3000590e720d975c9d0853c2cd8b378b691cd94029c83","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-01T17:59:33Z","title_canon_sha256":"4ea9a092ae5362654d5b39aa90e40da0991de5e44bc2fbab05f104a831375bb0"},"schema_version":"1.0","source":{"id":"2209.00647","kind":"arxiv","version":1}},"canonical_sha256":"5ebedd5fb910014c5c80ce970b5914f34f04a18cfe4470ef2153d6422c1b7532","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ebedd5fb910014c5c80ce970b5914f34f04a18cfe4470ef2153d6422c1b7532","first_computed_at":"2026-07-05T04:53:52.369271Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:53:52.369271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DjzYm7SRNtCA5C/5uacjmTO9vckE2JOkr1b54PiCMzYCXpUjsp02oCqBGT+sLkG7KRWujgGucKwW55RpSMD/Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:53:52.369646Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.00647","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3b56294946ebe0cb9517e6c1010fb378e220c40aaa1e6707494bf75ee850289","sha256:8ad6eab730fbd2d530d004b626754c4157d3a04923e4f9c0dacc5bac6044696b"],"state_sha256":"75918cf37edb321108655bcd05835032e207e82765c2aeaba51d0eb8ab406f92"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1SrIMEWWI6bpwpYNtxF0nzBF0xQUmKo3kqkIUtvRXoRJQTtfMlF/OxP84AWa+qjQPi3sIcIe40MN3BZnLoY/CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T07:05:16.609870Z","bundle_sha256":"df4484bfb42108c77f5f58f7d182f7f40b63ed071396b094eb34b3b8efc7517e"}}