{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:F27ECW5THOW6IZNA4BT6X4U4NX","short_pith_number":"pith:F27ECW5T","schema_version":"1.0","canonical_sha256":"2ebe415bb33bade465a0e067ebf29c6dcd5569472067289a20d5765577a7820a","source":{"kind":"arxiv","id":"1910.11285","version":3},"attestation_state":"computed","paper":{"title":"Towards Train-Test Consistency for Semi-supervised Temporal Action Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Shih-Fu Chang, Xudong Lin, Zheng Shou","submitted_at":"2019-10-24T17:00:14Z","abstract_excerpt":"Recently, Weakly-supervised Temporal Action Localization (WTAL) has been densely studied but there is still a large gap between weakly-supervised models and fully-supervised models. It is practical and intuitive to annotate temporal boundaries of a few examples and utilize them to help WTAL models better detect actions. However, the train-test discrepancy of action localization strategy prevents WTAL models from leveraging semi-supervision for further improvement. At training time, attention or multiple instance learning is used to aggregate predictions of each snippet for video-level classifi"},"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":"1910.11285","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-10-24T17:00:14Z","cross_cats_sorted":[],"title_canon_sha256":"0703a95173d05f9dacb65c54d045fcd10d248bd280f991a1866b6d1ef13fac60","abstract_canon_sha256":"b723b811daf2b83b1a3d9b74d693f7fcce1e1b97a48f10986f8ef86ac72914b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:49:39.097532Z","signature_b64":"iHmkUuuMcdEp6nPgcmWTG4o4T0U7Bt0vmB4VXbkUalUFmpxUwl0a2SR8VHWiTMOce99MOR+qCEOAw6EMf5DCAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ebe415bb33bade465a0e067ebf29c6dcd5569472067289a20d5765577a7820a","last_reissued_at":"2026-07-05T00:49:39.097145Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:49:39.097145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Train-Test Consistency for Semi-supervised Temporal Action Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Shih-Fu Chang, Xudong Lin, Zheng Shou","submitted_at":"2019-10-24T17:00:14Z","abstract_excerpt":"Recently, Weakly-supervised Temporal Action Localization (WTAL) has been densely studied but there is still a large gap between weakly-supervised models and fully-supervised models. It is practical and intuitive to annotate temporal boundaries of a few examples and utilize them to help WTAL models better detect actions. However, the train-test discrepancy of action localization strategy prevents WTAL models from leveraging semi-supervision for further improvement. At training time, attention or multiple instance learning is used to aggregate predictions of each snippet for video-level classifi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.11285","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/1910.11285/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":"1910.11285","created_at":"2026-07-05T00:49:39.097202+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.11285v3","created_at":"2026-07-05T00:49:39.097202+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.11285","created_at":"2026-07-05T00:49:39.097202+00:00"},{"alias_kind":"pith_short_12","alias_value":"F27ECW5THOW6","created_at":"2026-07-05T00:49:39.097202+00:00"},{"alias_kind":"pith_short_16","alias_value":"F27ECW5THOW6IZNA","created_at":"2026-07-05T00:49:39.097202+00:00"},{"alias_kind":"pith_short_8","alias_value":"F27ECW5T","created_at":"2026-07-05T00:49:39.097202+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.00467","citing_title":"SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning","ref_index":80,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F27ECW5THOW6IZNA4BT6X4U4NX","json":"https://pith.science/pith/F27ECW5THOW6IZNA4BT6X4U4NX.json","graph_json":"https://pith.science/api/pith-number/F27ECW5THOW6IZNA4BT6X4U4NX/graph.json","events_json":"https://pith.science/api/pith-number/F27ECW5THOW6IZNA4BT6X4U4NX/events.json","paper":"https://pith.science/paper/F27ECW5T"},"agent_actions":{"view_html":"https://pith.science/pith/F27ECW5THOW6IZNA4BT6X4U4NX","download_json":"https://pith.science/pith/F27ECW5THOW6IZNA4BT6X4U4NX.json","view_paper":"https://pith.science/paper/F27ECW5T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.11285&json=true","fetch_graph":"https://pith.science/api/pith-number/F27ECW5THOW6IZNA4BT6X4U4NX/graph.json","fetch_events":"https://pith.science/api/pith-number/F27ECW5THOW6IZNA4BT6X4U4NX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F27ECW5THOW6IZNA4BT6X4U4NX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F27ECW5THOW6IZNA4BT6X4U4NX/action/storage_attestation","attest_author":"https://pith.science/pith/F27ECW5THOW6IZNA4BT6X4U4NX/action/author_attestation","sign_citation":"https://pith.science/pith/F27ECW5THOW6IZNA4BT6X4U4NX/action/citation_signature","submit_replication":"https://pith.science/pith/F27ECW5THOW6IZNA4BT6X4U4NX/action/replication_record"}},"created_at":"2026-07-05T00:49:39.097202+00:00","updated_at":"2026-07-05T00:49:39.097202+00:00"}