{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ARKJRYKLRSLHG43NAFZINEV3UB","short_pith_number":"pith:ARKJRYKL","canonical_record":{"source":{"id":"2202.02317","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-04T18:58:36Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"b45c6ef31868b7495fc0dd3c8c4b7276387a4b171da92b7dc4fb42d665fcf23b","abstract_canon_sha256":"9574b5edc62e8d5cf46f3cecbbfd8183e16193533ba8748fc102dd3d605bbc5b"},"schema_version":"1.0"},"canonical_sha256":"045498e14b8c9673736d01728692bba04b8ddcdea96cfbb8645147ef9752a3a1","source":{"kind":"arxiv","id":"2202.02317","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.02317","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"arxiv_version","alias_value":"2202.02317v2","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.02317","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"pith_short_12","alias_value":"ARKJRYKLRSLH","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"pith_short_16","alias_value":"ARKJRYKLRSLHG43N","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"pith_short_8","alias_value":"ARKJRYKL","created_at":"2026-07-05T04:42:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ARKJRYKLRSLHG43NAFZINEV3UB","target":"record","payload":{"canonical_record":{"source":{"id":"2202.02317","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-04T18:58:36Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"b45c6ef31868b7495fc0dd3c8c4b7276387a4b171da92b7dc4fb42d665fcf23b","abstract_canon_sha256":"9574b5edc62e8d5cf46f3cecbbfd8183e16193533ba8748fc102dd3d605bbc5b"},"schema_version":"1.0"},"canonical_sha256":"045498e14b8c9673736d01728692bba04b8ddcdea96cfbb8645147ef9752a3a1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:42:16.827402Z","signature_b64":"KkdU0fP1ymtYqmM6sewdHQb/W+t5ewUyCs1Z5cSg6BtVQvkSApkOdKVX3Nc4426hlrNiIPD/jCqqB5p8YTubDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"045498e14b8c9673736d01728692bba04b8ddcdea96cfbb8645147ef9752a3a1","last_reissued_at":"2026-07-05T04:42:16.827047Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:42:16.827047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.02317","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":"2026-07-05T04:42:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pWoU5tYPuGG9hmOxEym7A9R41Ju1T9buwBpwfprvm8PGb6+cWySidOewJF0thNfhTpNK5Ba5Hf2148pK8DL8BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:53:42.336583Z"},"content_sha256":"e497faacb96c875cc89ae90ef3fc06ef198fb157398a774b54ae6e231c792da0","schema_version":"1.0","event_id":"sha256:e497faacb96c875cc89ae90ef3fc06ef198fb157398a774b54ae6e231c792da0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ARKJRYKLRSLHG43NAFZINEV3UB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Webly Supervised Concept Expansion for General Purpose Vision Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Amita Kamath, Aniruddha Kembhavi, Christopher Clark, Derek Hoiem, Eric Kolve, Tanmay Gupta","submitted_at":"2022-02-04T18:58:36Z","abstract_excerpt":"General Purpose Vision (GPV) systems are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and concepts from large fully supervised datasets. Scaling GPVs to tens of thousands of concepts by acquiring data to learn each concept for every skill quickly becomes prohibitive. This work presents an effective and inexpensive alternative: learn skills from supervised datasets, learn concepts from web image search, and leverage a key characteristic of GPVs: the ability to transfer visual knowledge across skil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.02317","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/2202.02317/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":"2026-07-05T04:42:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0fj6KYs07kl7praFQC1ENIZoxfpfnAL2ox94rp7hYXgNjFlf9jELNi0DhMo/reZU4vRl/TjyWSji523bw/oqDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:53:42.336955Z"},"content_sha256":"f509763127b851f51c64b36a333199a7a7eebce7be1c79a1acf5b2a75d66b4f0","schema_version":"1.0","event_id":"sha256:f509763127b851f51c64b36a333199a7a7eebce7be1c79a1acf5b2a75d66b4f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ARKJRYKLRSLHG43NAFZINEV3UB/bundle.json","state_url":"https://pith.science/pith/ARKJRYKLRSLHG43NAFZINEV3UB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ARKJRYKLRSLHG43NAFZINEV3UB/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":"2026-07-07T15:53:42Z","links":{"resolver":"https://pith.science/pith/ARKJRYKLRSLHG43NAFZINEV3UB","bundle":"https://pith.science/pith/ARKJRYKLRSLHG43NAFZINEV3UB/bundle.json","state":"https://pith.science/pith/ARKJRYKLRSLHG43NAFZINEV3UB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ARKJRYKLRSLHG43NAFZINEV3UB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ARKJRYKLRSLHG43NAFZINEV3UB","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":"9574b5edc62e8d5cf46f3cecbbfd8183e16193533ba8748fc102dd3d605bbc5b","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-04T18:58:36Z","title_canon_sha256":"b45c6ef31868b7495fc0dd3c8c4b7276387a4b171da92b7dc4fb42d665fcf23b"},"schema_version":"1.0","source":{"id":"2202.02317","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.02317","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"arxiv_version","alias_value":"2202.02317v2","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.02317","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"pith_short_12","alias_value":"ARKJRYKLRSLH","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"pith_short_16","alias_value":"ARKJRYKLRSLHG43N","created_at":"2026-07-05T04:42:16Z"},{"alias_kind":"pith_short_8","alias_value":"ARKJRYKL","created_at":"2026-07-05T04:42:16Z"}],"graph_snapshots":[{"event_id":"sha256:f509763127b851f51c64b36a333199a7a7eebce7be1c79a1acf5b2a75d66b4f0","target":"graph","created_at":"2026-07-05T04:42:16Z","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/2202.02317/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"General Purpose Vision (GPV) systems are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and concepts from large fully supervised datasets. Scaling GPVs to tens of thousands of concepts by acquiring data to learn each concept for every skill quickly becomes prohibitive. This work presents an effective and inexpensive alternative: learn skills from supervised datasets, learn concepts from web image search, and leverage a key characteristic of GPVs: the ability to transfer visual knowledge across skil","authors_text":"Amita Kamath, Aniruddha Kembhavi, Christopher Clark, Derek Hoiem, Eric Kolve, Tanmay Gupta","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-04T18:58:36Z","title":"Webly Supervised Concept Expansion for General Purpose Vision Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.02317","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:e497faacb96c875cc89ae90ef3fc06ef198fb157398a774b54ae6e231c792da0","target":"record","created_at":"2026-07-05T04:42:16Z","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":"9574b5edc62e8d5cf46f3cecbbfd8183e16193533ba8748fc102dd3d605bbc5b","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-02-04T18:58:36Z","title_canon_sha256":"b45c6ef31868b7495fc0dd3c8c4b7276387a4b171da92b7dc4fb42d665fcf23b"},"schema_version":"1.0","source":{"id":"2202.02317","kind":"arxiv","version":2}},"canonical_sha256":"045498e14b8c9673736d01728692bba04b8ddcdea96cfbb8645147ef9752a3a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"045498e14b8c9673736d01728692bba04b8ddcdea96cfbb8645147ef9752a3a1","first_computed_at":"2026-07-05T04:42:16.827047Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:42:16.827047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KkdU0fP1ymtYqmM6sewdHQb/W+t5ewUyCs1Z5cSg6BtVQvkSApkOdKVX3Nc4426hlrNiIPD/jCqqB5p8YTubDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:42:16.827402Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.02317","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e497faacb96c875cc89ae90ef3fc06ef198fb157398a774b54ae6e231c792da0","sha256:f509763127b851f51c64b36a333199a7a7eebce7be1c79a1acf5b2a75d66b4f0"],"state_sha256":"4bee685ded7eb995f4a7b579dbdd90c95b11ae420b8de74d6de81e4e89fafd2e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nuFmvQIwydckgnwp8y3RWfkc6BXv/vPqrqm5VrIvipZPtsM0fDccLR9USriUsnsfngs6hO22EKlD+4zcwLUyBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:53:42.338878Z","bundle_sha256":"741a032244ce3a9865dc7c36039029b5b21236a46d0f4c563b4a1c60b14ef0d8"}}