{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2YL6KC5EXIOR3OOLIEPVOIL7MH","short_pith_number":"pith:2YL6KC5E","schema_version":"1.0","canonical_sha256":"d617e50ba4ba1d1db9cb411f57217f61c8213daa4fd7ce8c12c88d0cec825a89","source":{"kind":"arxiv","id":"2208.14882","version":2},"attestation_state":"computed","paper":{"title":"Hierarchical Local-Global Transformer for Temporal Sentence Grounding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV","cs.IR"],"primary_cat":"cs.MM","authors_text":"Daizong Liu, Pan Zhou, Ruixuan Li, Xiang Fang, Zichuan Xu","submitted_at":"2022-08-31T14:16:56Z","abstract_excerpt":"This paper studies the multimedia problem of temporal sentence grounding (TSG), which aims to accurately determine the specific video segment in an untrimmed video according to a given sentence query. Traditional TSG methods mainly follow the top-down or bottom-up framework and are not end-to-end. They severely rely on time-consuming post-processing to refine the grounding results. Recently, some transformer-based approaches are proposed to efficiently and effectively model the fine-grained semantic alignment between video and query. Although these methods achieve significant performance to so"},"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":"2208.14882","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2022-08-31T14:16:56Z","cross_cats_sorted":["cs.CL","cs.CV","cs.IR"],"title_canon_sha256":"dc81417641dd5bd9eeab220d0575cd118b972e554952436def838aeb9c137246","abstract_canon_sha256":"4acbf62d5e1557c58272beb26e5641683d810fa1c789bd45be780cf974d6ddf7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:03:44.134954Z","signature_b64":"o2IwKxKG2Nscw8XwZcJwqFUK1MYCi64wiVPGnvX4UyK6ufrfpoZ5xzKNczGw4HjX7YBXRrfq9Q90zYohssQEDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d617e50ba4ba1d1db9cb411f57217f61c8213daa4fd7ce8c12c88d0cec825a89","last_reissued_at":"2026-05-26T02:03:44.134282Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:03:44.134282Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Local-Global Transformer for Temporal Sentence Grounding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV","cs.IR"],"primary_cat":"cs.MM","authors_text":"Daizong Liu, Pan Zhou, Ruixuan Li, Xiang Fang, Zichuan Xu","submitted_at":"2022-08-31T14:16:56Z","abstract_excerpt":"This paper studies the multimedia problem of temporal sentence grounding (TSG), which aims to accurately determine the specific video segment in an untrimmed video according to a given sentence query. Traditional TSG methods mainly follow the top-down or bottom-up framework and are not end-to-end. They severely rely on time-consuming post-processing to refine the grounding results. Recently, some transformer-based approaches are proposed to efficiently and effectively model the fine-grained semantic alignment between video and query. Although these methods achieve significant performance to so"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.14882","kind":"arxiv","version":2},"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/2208.14882/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":"2208.14882","created_at":"2026-05-26T02:03:44.134387+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.14882v2","created_at":"2026-05-26T02:03:44.134387+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.14882","created_at":"2026-05-26T02:03:44.134387+00:00"},{"alias_kind":"pith_short_12","alias_value":"2YL6KC5EXIOR","created_at":"2026-05-26T02:03:44.134387+00:00"},{"alias_kind":"pith_short_16","alias_value":"2YL6KC5EXIOR3OOL","created_at":"2026-05-26T02:03:44.134387+00:00"},{"alias_kind":"pith_short_8","alias_value":"2YL6KC5E","created_at":"2026-05-26T02:03:44.134387+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/2YL6KC5EXIOR3OOLIEPVOIL7MH","json":"https://pith.science/pith/2YL6KC5EXIOR3OOLIEPVOIL7MH.json","graph_json":"https://pith.science/api/pith-number/2YL6KC5EXIOR3OOLIEPVOIL7MH/graph.json","events_json":"https://pith.science/api/pith-number/2YL6KC5EXIOR3OOLIEPVOIL7MH/events.json","paper":"https://pith.science/paper/2YL6KC5E"},"agent_actions":{"view_html":"https://pith.science/pith/2YL6KC5EXIOR3OOLIEPVOIL7MH","download_json":"https://pith.science/pith/2YL6KC5EXIOR3OOLIEPVOIL7MH.json","view_paper":"https://pith.science/paper/2YL6KC5E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.14882&json=true","fetch_graph":"https://pith.science/api/pith-number/2YL6KC5EXIOR3OOLIEPVOIL7MH/graph.json","fetch_events":"https://pith.science/api/pith-number/2YL6KC5EXIOR3OOLIEPVOIL7MH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2YL6KC5EXIOR3OOLIEPVOIL7MH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2YL6KC5EXIOR3OOLIEPVOIL7MH/action/storage_attestation","attest_author":"https://pith.science/pith/2YL6KC5EXIOR3OOLIEPVOIL7MH/action/author_attestation","sign_citation":"https://pith.science/pith/2YL6KC5EXIOR3OOLIEPVOIL7MH/action/citation_signature","submit_replication":"https://pith.science/pith/2YL6KC5EXIOR3OOLIEPVOIL7MH/action/replication_record"}},"created_at":"2026-05-26T02:03:44.134387+00:00","updated_at":"2026-05-26T02:03:44.134387+00:00"}