{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:KHKSU3QHLYKOYK4RVIIO2OFZMO","short_pith_number":"pith:KHKSU3QH","canonical_record":{"source":{"id":"2207.05288","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-12T03:53:42Z","cross_cats_sorted":[],"title_canon_sha256":"7ec4852e4f73d4a040f0ef4ee10fa3190e9642f7af7ed8d3f9d32fadf7fdf50d","abstract_canon_sha256":"ea32fc544ad7364053298b2b35a073399293ad5d614e18d5b895213bbadcbded"},"schema_version":"1.0"},"canonical_sha256":"51d52a6e075e14ec2b91aa10ed38b963bda6278d5699a9ef547603692fcc2661","source":{"kind":"arxiv","id":"2207.05288","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.05288","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"arxiv_version","alias_value":"2207.05288v1","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.05288","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"pith_short_12","alias_value":"KHKSU3QHLYKO","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"pith_short_16","alias_value":"KHKSU3QHLYKOYK4R","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"pith_short_8","alias_value":"KHKSU3QH","created_at":"2026-07-05T04:39:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:KHKSU3QHLYKOYK4RVIIO2OFZMO","target":"record","payload":{"canonical_record":{"source":{"id":"2207.05288","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-12T03:53:42Z","cross_cats_sorted":[],"title_canon_sha256":"7ec4852e4f73d4a040f0ef4ee10fa3190e9642f7af7ed8d3f9d32fadf7fdf50d","abstract_canon_sha256":"ea32fc544ad7364053298b2b35a073399293ad5d614e18d5b895213bbadcbded"},"schema_version":"1.0"},"canonical_sha256":"51d52a6e075e14ec2b91aa10ed38b963bda6278d5699a9ef547603692fcc2661","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:39:40.956793Z","signature_b64":"S49FxSxdcC5+txWdr/1xzhQdRagjXtbKgcTy2TcZJhu1HPm0S/HkibRBlli8PvEx18LjryOHiMKq2DFT5xeJDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51d52a6e075e14ec2b91aa10ed38b963bda6278d5699a9ef547603692fcc2661","last_reissued_at":"2026-07-05T04:39:40.956466Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:39:40.956466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.05288","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":"2026-07-05T04:39:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QKnHPPazrK3+doS69BMwnq0KKkCEhg0sGlR1WslGDUxBgbaK01mPOFODKknYsW1BVuU9XVH0Ykc0zAZrUwSYDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:30:51.898811Z"},"content_sha256":"ca78875221338394610ed5c4a069178a2160fa1c2e83c29c119186380bb1825b","schema_version":"1.0","event_id":"sha256:ca78875221338394610ed5c4a069178a2160fa1c2e83c29c119186380bb1825b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:KHKSU3QHLYKOYK4RVIIO2OFZMO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MetaAge: Meta-Learning Personalized Age Estimators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abudukelimu Wuerkaixi, Jianjiang Feng, Jie Zhou, Jiwen Lu, Wanhua Li","submitted_at":"2022-07-12T03:53:42Z","abstract_excerpt":"Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods suffer from the lack of large-scale datasets due to the high-level requirements: identity labels and enough samples for each person to form a long-term aging pattern. In this paper, we aim to learn personalized age estimators without the above requirements and propose a meta-learning method named MetaAge for age estimation. Unlike most existing per"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.05288","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/2207.05288/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":"2026-07-05T04:39:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R8hd0pAzpkqu+cqP496u77TkKwaiiSpn1e/Y0M8d7jKoR2k2hlTq+4R80WPDJDtUSecoa+Hqp5eThcEdY+uTBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:30:51.899178Z"},"content_sha256":"c8b8b32e630e9747241a984798370ccd07c8f70a8051b0fb02c0fceda3c7482a","schema_version":"1.0","event_id":"sha256:c8b8b32e630e9747241a984798370ccd07c8f70a8051b0fb02c0fceda3c7482a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KHKSU3QHLYKOYK4RVIIO2OFZMO/bundle.json","state_url":"https://pith.science/pith/KHKSU3QHLYKOYK4RVIIO2OFZMO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KHKSU3QHLYKOYK4RVIIO2OFZMO/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":"2026-07-07T15:30:51Z","links":{"resolver":"https://pith.science/pith/KHKSU3QHLYKOYK4RVIIO2OFZMO","bundle":"https://pith.science/pith/KHKSU3QHLYKOYK4RVIIO2OFZMO/bundle.json","state":"https://pith.science/pith/KHKSU3QHLYKOYK4RVIIO2OFZMO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KHKSU3QHLYKOYK4RVIIO2OFZMO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:KHKSU3QHLYKOYK4RVIIO2OFZMO","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":"ea32fc544ad7364053298b2b35a073399293ad5d614e18d5b895213bbadcbded","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-12T03:53:42Z","title_canon_sha256":"7ec4852e4f73d4a040f0ef4ee10fa3190e9642f7af7ed8d3f9d32fadf7fdf50d"},"schema_version":"1.0","source":{"id":"2207.05288","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.05288","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"arxiv_version","alias_value":"2207.05288v1","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.05288","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"pith_short_12","alias_value":"KHKSU3QHLYKO","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"pith_short_16","alias_value":"KHKSU3QHLYKOYK4R","created_at":"2026-07-05T04:39:40Z"},{"alias_kind":"pith_short_8","alias_value":"KHKSU3QH","created_at":"2026-07-05T04:39:40Z"}],"graph_snapshots":[{"event_id":"sha256:c8b8b32e630e9747241a984798370ccd07c8f70a8051b0fb02c0fceda3c7482a","target":"graph","created_at":"2026-07-05T04:39:40Z","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/2207.05288/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods suffer from the lack of large-scale datasets due to the high-level requirements: identity labels and enough samples for each person to form a long-term aging pattern. In this paper, we aim to learn personalized age estimators without the above requirements and propose a meta-learning method named MetaAge for age estimation. Unlike most existing per","authors_text":"Abudukelimu Wuerkaixi, Jianjiang Feng, Jie Zhou, Jiwen Lu, Wanhua Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-12T03:53:42Z","title":"MetaAge: Meta-Learning Personalized Age Estimators"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.05288","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:ca78875221338394610ed5c4a069178a2160fa1c2e83c29c119186380bb1825b","target":"record","created_at":"2026-07-05T04:39:40Z","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":"ea32fc544ad7364053298b2b35a073399293ad5d614e18d5b895213bbadcbded","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-12T03:53:42Z","title_canon_sha256":"7ec4852e4f73d4a040f0ef4ee10fa3190e9642f7af7ed8d3f9d32fadf7fdf50d"},"schema_version":"1.0","source":{"id":"2207.05288","kind":"arxiv","version":1}},"canonical_sha256":"51d52a6e075e14ec2b91aa10ed38b963bda6278d5699a9ef547603692fcc2661","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"51d52a6e075e14ec2b91aa10ed38b963bda6278d5699a9ef547603692fcc2661","first_computed_at":"2026-07-05T04:39:40.956466Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:39:40.956466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S49FxSxdcC5+txWdr/1xzhQdRagjXtbKgcTy2TcZJhu1HPm0S/HkibRBlli8PvEx18LjryOHiMKq2DFT5xeJDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:39:40.956793Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.05288","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ca78875221338394610ed5c4a069178a2160fa1c2e83c29c119186380bb1825b","sha256:c8b8b32e630e9747241a984798370ccd07c8f70a8051b0fb02c0fceda3c7482a"],"state_sha256":"d942c0e6e94ed590752744b23be58e72768a34f753013234cd3121af098d6841"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hk/xvbc0DDbaIbElr+p8hbWflUjXMlXw7gZ+vXykB4BCyDDHWrkif7H0upcLfLhl3NoqMXvuqSy1Abq/kvoLBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:30:51.901137Z","bundle_sha256":"653ac08eb196cfaf4f5251ffdb3658a5960835b5b8cb38e0ab47338ffcaaf864"}}