{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UA3CCSBPCOI5OTD267PKLKUIUI","short_pith_number":"pith:UA3CCSBP","schema_version":"1.0","canonical_sha256":"a03621482f1391d74c7af7dea5aa88a22337346470e661d9ccce89ee12644229","source":{"kind":"arxiv","id":"2501.09565","version":1},"attestation_state":"computed","paper":{"title":"A New Teacher-Reviewer-Student Framework for Semi-supervised 2D Human Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fei Peng, Huadong Ma, Mengshi Qi, Wulian Yun","submitted_at":"2025-01-16T14:40:02Z","abstract_excerpt":"Conventional 2D human pose estimation methods typically require extensive labeled annotations, which are both labor-intensive and expensive. In contrast, semi-supervised 2D human pose estimation can alleviate the above problems by leveraging a large amount of unlabeled data along with a small portion of labeled data. Existing semi-supervised 2D human pose estimation methods update the network through backpropagation, ignoring crucial historical information from the previous training process. Therefore, we propose a novel semi-supervised 2D human pose estimation method by utilizing a newly desi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.09565","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-16T14:40:02Z","cross_cats_sorted":[],"title_canon_sha256":"42a0d1c56a79fc0e73bc3598be0bd93ac7292c34a359fbbaeb6fd695e30b672f","abstract_canon_sha256":"1be539881bc2ed9a5bc9a5fa6b5c90a8a16c02dd9a3329701c8673135452120d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:01:49.054058Z","signature_b64":"9aerccW7B0i9VBH9kHpfxMLes3fAA+JOUzTW5Q2akIv2MgkRK1GORNd4hPC57AA7NSQXI3BYzWBuhOeNNwByAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a03621482f1391d74c7af7dea5aa88a22337346470e661d9ccce89ee12644229","last_reissued_at":"2026-07-05T10:01:49.053593Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:01:49.053593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A New Teacher-Reviewer-Student Framework for Semi-supervised 2D Human Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fei Peng, Huadong Ma, Mengshi Qi, Wulian Yun","submitted_at":"2025-01-16T14:40:02Z","abstract_excerpt":"Conventional 2D human pose estimation methods typically require extensive labeled annotations, which are both labor-intensive and expensive. In contrast, semi-supervised 2D human pose estimation can alleviate the above problems by leveraging a large amount of unlabeled data along with a small portion of labeled data. Existing semi-supervised 2D human pose estimation methods update the network through backpropagation, ignoring crucial historical information from the previous training process. Therefore, we propose a novel semi-supervised 2D human pose estimation method by utilizing a newly desi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09565","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/2501.09565/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.09565","created_at":"2026-07-05T10:01:49.053664+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.09565v1","created_at":"2026-07-05T10:01:49.053664+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09565","created_at":"2026-07-05T10:01:49.053664+00:00"},{"alias_kind":"pith_short_12","alias_value":"UA3CCSBPCOI5","created_at":"2026-07-05T10:01:49.053664+00:00"},{"alias_kind":"pith_short_16","alias_value":"UA3CCSBPCOI5OTD2","created_at":"2026-07-05T10:01:49.053664+00:00"},{"alias_kind":"pith_short_8","alias_value":"UA3CCSBP","created_at":"2026-07-05T10:01:49.053664+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UA3CCSBPCOI5OTD267PKLKUIUI","json":"https://pith.science/pith/UA3CCSBPCOI5OTD267PKLKUIUI.json","graph_json":"https://pith.science/api/pith-number/UA3CCSBPCOI5OTD267PKLKUIUI/graph.json","events_json":"https://pith.science/api/pith-number/UA3CCSBPCOI5OTD267PKLKUIUI/events.json","paper":"https://pith.science/paper/UA3CCSBP"},"agent_actions":{"view_html":"https://pith.science/pith/UA3CCSBPCOI5OTD267PKLKUIUI","download_json":"https://pith.science/pith/UA3CCSBPCOI5OTD267PKLKUIUI.json","view_paper":"https://pith.science/paper/UA3CCSBP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.09565&json=true","fetch_graph":"https://pith.science/api/pith-number/UA3CCSBPCOI5OTD267PKLKUIUI/graph.json","fetch_events":"https://pith.science/api/pith-number/UA3CCSBPCOI5OTD267PKLKUIUI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UA3CCSBPCOI5OTD267PKLKUIUI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UA3CCSBPCOI5OTD267PKLKUIUI/action/storage_attestation","attest_author":"https://pith.science/pith/UA3CCSBPCOI5OTD267PKLKUIUI/action/author_attestation","sign_citation":"https://pith.science/pith/UA3CCSBPCOI5OTD267PKLKUIUI/action/citation_signature","submit_replication":"https://pith.science/pith/UA3CCSBPCOI5OTD267PKLKUIUI/action/replication_record"}},"created_at":"2026-07-05T10:01:49.053664+00:00","updated_at":"2026-07-05T10:01:49.053664+00:00"}