{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7VG5X7T237T4QSH2B4ZOG6BJO7","short_pith_number":"pith:7VG5X7T2","schema_version":"1.0","canonical_sha256":"fd4ddbfe7adfe7c848fa0f32e3782977c3be8611a149c9d6ee612f28feb19e53","source":{"kind":"arxiv","id":"2409.10473","version":1},"attestation_state":"computed","paper":{"title":"MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jiahang Zhang, Jiaying Liu, Lehong Wu, Lilang Lin, Yiyang Ma","submitted_at":"2024-09-16T17:06:10Z","abstract_excerpt":"Self-supervised learning has proved effective for skeleton-based human action understanding. However, previous works either rely on contrastive learning that suffers false negative problems or are based on reconstruction that learns too much unessential low-level clues, leading to limited representations for downstream tasks. Recently, great advances have been made in generative learning, which is naturally a challenging yet meaningful pretext task to model the general underlying data distributions. However, the representation learning capacity of generative models is under-explored, especiall"},"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":"2409.10473","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-16T17:06:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e030edbe95f77f998bf07b6a1c8cb9eae7895ccfc7b03a645c672fa5112b8117","abstract_canon_sha256":"4aafddf9b3cf5fba24903fc73a4ac364019656d9838f375110d7746960b98bc6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:40.721597Z","signature_b64":"bA35Rl7N8md96BoPqnyM3ln/chZRVAcmt84XqVzbwTAzfzToXu6rgSEy9AWyu08os99VGQyl4VYZhbaSGjn1AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd4ddbfe7adfe7c848fa0f32e3782977c3be8611a149c9d6ee612f28feb19e53","last_reissued_at":"2026-07-05T09:07:40.721131Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:40.721131Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jiahang Zhang, Jiaying Liu, Lehong Wu, Lilang Lin, Yiyang Ma","submitted_at":"2024-09-16T17:06:10Z","abstract_excerpt":"Self-supervised learning has proved effective for skeleton-based human action understanding. However, previous works either rely on contrastive learning that suffers false negative problems or are based on reconstruction that learns too much unessential low-level clues, leading to limited representations for downstream tasks. Recently, great advances have been made in generative learning, which is naturally a challenging yet meaningful pretext task to model the general underlying data distributions. However, the representation learning capacity of generative models is under-explored, especiall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.10473","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/2409.10473/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":"2409.10473","created_at":"2026-07-05T09:07:40.721189+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.10473v1","created_at":"2026-07-05T09:07:40.721189+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.10473","created_at":"2026-07-05T09:07:40.721189+00:00"},{"alias_kind":"pith_short_12","alias_value":"7VG5X7T237T4","created_at":"2026-07-05T09:07:40.721189+00:00"},{"alias_kind":"pith_short_16","alias_value":"7VG5X7T237T4QSH2","created_at":"2026-07-05T09:07:40.721189+00:00"},{"alias_kind":"pith_short_8","alias_value":"7VG5X7T2","created_at":"2026-07-05T09:07:40.721189+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/7VG5X7T237T4QSH2B4ZOG6BJO7","json":"https://pith.science/pith/7VG5X7T237T4QSH2B4ZOG6BJO7.json","graph_json":"https://pith.science/api/pith-number/7VG5X7T237T4QSH2B4ZOG6BJO7/graph.json","events_json":"https://pith.science/api/pith-number/7VG5X7T237T4QSH2B4ZOG6BJO7/events.json","paper":"https://pith.science/paper/7VG5X7T2"},"agent_actions":{"view_html":"https://pith.science/pith/7VG5X7T237T4QSH2B4ZOG6BJO7","download_json":"https://pith.science/pith/7VG5X7T237T4QSH2B4ZOG6BJO7.json","view_paper":"https://pith.science/paper/7VG5X7T2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.10473&json=true","fetch_graph":"https://pith.science/api/pith-number/7VG5X7T237T4QSH2B4ZOG6BJO7/graph.json","fetch_events":"https://pith.science/api/pith-number/7VG5X7T237T4QSH2B4ZOG6BJO7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7VG5X7T237T4QSH2B4ZOG6BJO7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7VG5X7T237T4QSH2B4ZOG6BJO7/action/storage_attestation","attest_author":"https://pith.science/pith/7VG5X7T237T4QSH2B4ZOG6BJO7/action/author_attestation","sign_citation":"https://pith.science/pith/7VG5X7T237T4QSH2B4ZOG6BJO7/action/citation_signature","submit_replication":"https://pith.science/pith/7VG5X7T237T4QSH2B4ZOG6BJO7/action/replication_record"}},"created_at":"2026-07-05T09:07:40.721189+00:00","updated_at":"2026-07-05T09:07:40.721189+00:00"}