{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:TV2QA6H5VY6NTTCF35ZLJGVRMI","short_pith_number":"pith:TV2QA6H5","canonical_record":{"source":{"id":"2209.12255","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-25T16:23:12Z","cross_cats_sorted":[],"title_canon_sha256":"3947a24fba2ff49ae7653f5efd8df1884694c8bf7d990681fc3da3c5180b75bc","abstract_canon_sha256":"b6193d2267412cd228eda8eed214f4dd4c81423c3a7163c0e56f5797b8df32e5"},"schema_version":"1.0"},"canonical_sha256":"9d750078fdae3cd9cc45df72b49ab1623d0dec98429f9e901e75e1c2a9a801d2","source":{"kind":"arxiv","id":"2209.12255","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.12255","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"arxiv_version","alias_value":"2209.12255v2","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.12255","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"pith_short_12","alias_value":"TV2QA6H5VY6N","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"pith_short_16","alias_value":"TV2QA6H5VY6NTTCF","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"pith_short_8","alias_value":"TV2QA6H5","created_at":"2026-07-05T05:13:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:TV2QA6H5VY6NTTCF35ZLJGVRMI","target":"record","payload":{"canonical_record":{"source":{"id":"2209.12255","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-25T16:23:12Z","cross_cats_sorted":[],"title_canon_sha256":"3947a24fba2ff49ae7653f5efd8df1884694c8bf7d990681fc3da3c5180b75bc","abstract_canon_sha256":"b6193d2267412cd228eda8eed214f4dd4c81423c3a7163c0e56f5797b8df32e5"},"schema_version":"1.0"},"canonical_sha256":"9d750078fdae3cd9cc45df72b49ab1623d0dec98429f9e901e75e1c2a9a801d2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:38.006286Z","signature_b64":"UlNthuYHafcdq24RqvEt7aiRHpwiw7kzBECwJ8kXrCFr98wNcmGLA9Ib+0WUrx7pn5Fj6Q4f9V5mS5Ikw5CNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d750078fdae3cd9cc45df72b49ab1623d0dec98429f9e901e75e1c2a9a801d2","last_reissued_at":"2026-07-05T05:13:38.005830Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:38.005830Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.12255","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":"2026-07-05T05:13:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o5ATVSFQekGnKvqbDjx+5g2F98KazK45JGhOcTKVP1/3kg06TC2L6ChWSKbwNjiT/B46Jn3USlQPIem4uSF3Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T06:03:13.677420Z"},"content_sha256":"637be288557ba3d2ec05ffadd192e50c26422fb53d965099c8f9b09601780896","schema_version":"1.0","event_id":"sha256:637be288557ba3d2ec05ffadd192e50c26422fb53d965099c8f9b09601780896"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:TV2QA6H5VY6NTTCF35ZLJGVRMI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Collaboration of Pre-trained Models Makes Better Few-shot Learner","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bohao Li, Hao Dong, Hongsheng Li, Peng Gao, Renrui Zhang, Wei Zhang, Yu Qiao","submitted_at":"2022-09-25T16:23:12Z","abstract_excerpt":"Few-shot classification requires deep neural networks to learn generalized representations only from limited training images, which is challenging but significant in low-data regimes. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. Based on this point, we question if the large-scale pre-training can alleviate the few-shot data deficiency and also assist the representation learning by the pre-learned knowledge. In this paper, we propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior k"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.12255","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/2209.12255/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":"2026-07-05T05:13:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gloljSTDDR4w0kNdQQMCjad9ols0DTF47QCfbLkEKm9rB76gUapxMw1g0Ml2ZDCPRssV0pUsg/tbZZ13R5+6Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T06:03:13.678090Z"},"content_sha256":"d9e46a536129074b6429675517581039ab14414e363f1db931d0fe88dca6a816","schema_version":"1.0","event_id":"sha256:d9e46a536129074b6429675517581039ab14414e363f1db931d0fe88dca6a816"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TV2QA6H5VY6NTTCF35ZLJGVRMI/bundle.json","state_url":"https://pith.science/pith/TV2QA6H5VY6NTTCF35ZLJGVRMI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TV2QA6H5VY6NTTCF35ZLJGVRMI/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":"2026-08-18T06:03:13Z","links":{"resolver":"https://pith.science/pith/TV2QA6H5VY6NTTCF35ZLJGVRMI","bundle":"https://pith.science/pith/TV2QA6H5VY6NTTCF35ZLJGVRMI/bundle.json","state":"https://pith.science/pith/TV2QA6H5VY6NTTCF35ZLJGVRMI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TV2QA6H5VY6NTTCF35ZLJGVRMI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:TV2QA6H5VY6NTTCF35ZLJGVRMI","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":"b6193d2267412cd228eda8eed214f4dd4c81423c3a7163c0e56f5797b8df32e5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-25T16:23:12Z","title_canon_sha256":"3947a24fba2ff49ae7653f5efd8df1884694c8bf7d990681fc3da3c5180b75bc"},"schema_version":"1.0","source":{"id":"2209.12255","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.12255","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"arxiv_version","alias_value":"2209.12255v2","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.12255","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"pith_short_12","alias_value":"TV2QA6H5VY6N","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"pith_short_16","alias_value":"TV2QA6H5VY6NTTCF","created_at":"2026-07-05T05:13:38Z"},{"alias_kind":"pith_short_8","alias_value":"TV2QA6H5","created_at":"2026-07-05T05:13:38Z"}],"graph_snapshots":[{"event_id":"sha256:d9e46a536129074b6429675517581039ab14414e363f1db931d0fe88dca6a816","target":"graph","created_at":"2026-07-05T05:13:38Z","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/2209.12255/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Few-shot classification requires deep neural networks to learn generalized representations only from limited training images, which is challenging but significant in low-data regimes. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. Based on this point, we question if the large-scale pre-training can alleviate the few-shot data deficiency and also assist the representation learning by the pre-learned knowledge. In this paper, we propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior k","authors_text":"Bohao Li, Hao Dong, Hongsheng Li, Peng Gao, Renrui Zhang, Wei Zhang, Yu Qiao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-25T16:23:12Z","title":"Collaboration of Pre-trained Models Makes Better Few-shot Learner"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.12255","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":"sha256:637be288557ba3d2ec05ffadd192e50c26422fb53d965099c8f9b09601780896","target":"record","created_at":"2026-07-05T05:13:38Z","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":"b6193d2267412cd228eda8eed214f4dd4c81423c3a7163c0e56f5797b8df32e5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-25T16:23:12Z","title_canon_sha256":"3947a24fba2ff49ae7653f5efd8df1884694c8bf7d990681fc3da3c5180b75bc"},"schema_version":"1.0","source":{"id":"2209.12255","kind":"arxiv","version":2}},"canonical_sha256":"9d750078fdae3cd9cc45df72b49ab1623d0dec98429f9e901e75e1c2a9a801d2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d750078fdae3cd9cc45df72b49ab1623d0dec98429f9e901e75e1c2a9a801d2","first_computed_at":"2026-07-05T05:13:38.005830Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:13:38.005830Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UlNthuYHafcdq24RqvEt7aiRHpwiw7kzBECwJ8kXrCFr98wNcmGLA9Ib+0WUrx7pn5Fj6Q4f9V5mS5Ikw5CNBw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:13:38.006286Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.12255","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:637be288557ba3d2ec05ffadd192e50c26422fb53d965099c8f9b09601780896","sha256:d9e46a536129074b6429675517581039ab14414e363f1db931d0fe88dca6a816"],"state_sha256":"ff61ba791c579b935009dbb55c9fad72a6c8e0181e07273fd0ac62fbfbf6a780"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qBu7RSjdoc9e3yK4Hy7NIAKuR450yoDti8uk30pTEDM+HGXTFKIZtAABOxIuKLHXMvIp4CHBmC8xC3mYv1cpDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T06:03:13.685309Z","bundle_sha256":"1a8728d4d2d008dd4a30e0af8fc2a2d499a155da321cbc7d0984d015c92bdf49"}}