{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:FU2ZWLVMVORDEYS23KRLJXWKG7","short_pith_number":"pith:FU2ZWLVM","schema_version":"1.0","canonical_sha256":"2d359b2eacaba232625adaa2b4deca37cf14ddc5f7f00e530ddbddf1e8f5af50","source":{"kind":"arxiv","id":"2211.10412","version":3},"attestation_state":"computed","paper":{"title":"Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haozhi Cao, Jianfei Yang, Lihua Xie, Xiaoli Li, Yuecong Xu, Zhenghua Chen","submitted_at":"2022-11-17T05:05:42Z","abstract_excerpt":"Video analysis tasks such as action recognition have received increasing research interest with growing applications in fields such as smart healthcare, thanks to the introduction of large-scale datasets and deep learning-based representations. However, video models trained on existing datasets suffer from significant performance degradation when deployed directly to real-world applications due to domain shifts between the training public video datasets (source video domains) and real-world videos (target video domains). Further, with the high cost of video annotation, it is more practical to "},"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":"2211.10412","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-17T05:05:42Z","cross_cats_sorted":[],"title_canon_sha256":"b51413ae168c9fe47436c167fc1b07bc28c10fd356886aa4671db293fb757319","abstract_canon_sha256":"7ab78943616dc7918715e48f254151b296e1a36f9b1d134a531d63562413e0f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:48:57.201087Z","signature_b64":"IrGwXmEbM48TR0QrwDcFih5JZ9DaKrJ/Sjy7rSDhW8EubeCeCnE//lVDqqF7KKgiHrIT2Fc5ESPwmM/v6AZ/Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d359b2eacaba232625adaa2b4deca37cf14ddc5f7f00e530ddbddf1e8f5af50","last_reissued_at":"2026-07-05T08:48:57.200667Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:48:57.200667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haozhi Cao, Jianfei Yang, Lihua Xie, Xiaoli Li, Yuecong Xu, Zhenghua Chen","submitted_at":"2022-11-17T05:05:42Z","abstract_excerpt":"Video analysis tasks such as action recognition have received increasing research interest with growing applications in fields such as smart healthcare, thanks to the introduction of large-scale datasets and deep learning-based representations. However, video models trained on existing datasets suffer from significant performance degradation when deployed directly to real-world applications due to domain shifts between the training public video datasets (source video domains) and real-world videos (target video domains). Further, with the high cost of video annotation, it is more practical to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.10412","kind":"arxiv","version":3},"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/2211.10412/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":"2211.10412","created_at":"2026-07-05T08:48:57.200727+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.10412v3","created_at":"2026-07-05T08:48:57.200727+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.10412","created_at":"2026-07-05T08:48:57.200727+00:00"},{"alias_kind":"pith_short_12","alias_value":"FU2ZWLVMVORD","created_at":"2026-07-05T08:48:57.200727+00:00"},{"alias_kind":"pith_short_16","alias_value":"FU2ZWLVMVORDEYS2","created_at":"2026-07-05T08:48:57.200727+00:00"},{"alias_kind":"pith_short_8","alias_value":"FU2ZWLVM","created_at":"2026-07-05T08:48:57.200727+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.16946","citing_title":"Video Domain Incremental Learning for Human Action Recognition in Home Environments","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FU2ZWLVMVORDEYS23KRLJXWKG7","json":"https://pith.science/pith/FU2ZWLVMVORDEYS23KRLJXWKG7.json","graph_json":"https://pith.science/api/pith-number/FU2ZWLVMVORDEYS23KRLJXWKG7/graph.json","events_json":"https://pith.science/api/pith-number/FU2ZWLVMVORDEYS23KRLJXWKG7/events.json","paper":"https://pith.science/paper/FU2ZWLVM"},"agent_actions":{"view_html":"https://pith.science/pith/FU2ZWLVMVORDEYS23KRLJXWKG7","download_json":"https://pith.science/pith/FU2ZWLVMVORDEYS23KRLJXWKG7.json","view_paper":"https://pith.science/paper/FU2ZWLVM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.10412&json=true","fetch_graph":"https://pith.science/api/pith-number/FU2ZWLVMVORDEYS23KRLJXWKG7/graph.json","fetch_events":"https://pith.science/api/pith-number/FU2ZWLVMVORDEYS23KRLJXWKG7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FU2ZWLVMVORDEYS23KRLJXWKG7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FU2ZWLVMVORDEYS23KRLJXWKG7/action/storage_attestation","attest_author":"https://pith.science/pith/FU2ZWLVMVORDEYS23KRLJXWKG7/action/author_attestation","sign_citation":"https://pith.science/pith/FU2ZWLVMVORDEYS23KRLJXWKG7/action/citation_signature","submit_replication":"https://pith.science/pith/FU2ZWLVMVORDEYS23KRLJXWKG7/action/replication_record"}},"created_at":"2026-07-05T08:48:57.200727+00:00","updated_at":"2026-07-05T08:48:57.200727+00:00"}