{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FYAADENTEFOJGJLZQCXBMNXHIX","short_pith_number":"pith:FYAADENT","schema_version":"1.0","canonical_sha256":"2e000191b3215c93257980ae1636e745ee502ec8d3885cc60cc0c42d4aba0d74","source":{"kind":"arxiv","id":"2605.14838","version":1},"attestation_state":"computed","paper":{"title":"Multi-proposal Collaboration and Multi-task Training for Weakly-supervised Video Moment Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Bin Jiang, Bolin Zhang, Chao Yang, Ichiro Ide, Takahiro Komamizu","submitted_at":"2026-05-14T13:43:32Z","abstract_excerpt":"This study focuses on weakly-supervised Video Moment Retrieval (VMR), aiming to identify a moment semantically similar to the given query within an untrimmed video using only video-level correspondences, without relying on temporal annotations during training. Previous methods either aggregate predictions for all instances in the video, or indirectly address the task by proposing reconstructions for the query. However, these methods often produce low-quality temporal proposals, struggle with distinguishing misaligned moments in the same video, or lack stability due to a reliance on a single au"},"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":"2605.14838","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T13:43:32Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"8b1388f302d9883fbc7a5c0956dd30cda820b2d3506eb583c75e9fc9c4aab217","abstract_canon_sha256":"3edde31b5264f350921bcd85b2eb2d86f719b49b2ecf4a55d8441b13c1dcd3f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:57.973402Z","signature_b64":"VtKlot3+/rtDT2yFSxmwozH/dPwtjX3+jd/ZVSgwOkVd3ebEgGbDTTo6EHA1zKx1zhbFVbH7S79JlGhZPfwYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e000191b3215c93257980ae1636e745ee502ec8d3885cc60cc0c42d4aba0d74","last_reissued_at":"2026-05-17T23:38:57.972680Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:57.972680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-proposal Collaboration and Multi-task Training for Weakly-supervised Video Moment Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Bin Jiang, Bolin Zhang, Chao Yang, Ichiro Ide, Takahiro Komamizu","submitted_at":"2026-05-14T13:43:32Z","abstract_excerpt":"This study focuses on weakly-supervised Video Moment Retrieval (VMR), aiming to identify a moment semantically similar to the given query within an untrimmed video using only video-level correspondences, without relying on temporal annotations during training. Previous methods either aggregate predictions for all instances in the video, or indirectly address the task by proposing reconstructions for the query. However, these methods often produce low-quality temporal proposals, struggle with distinguishing misaligned moments in the same video, or lack stability due to a reliance on a single au"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.14838","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":""},"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":"2605.14838","created_at":"2026-05-17T23:38:57.972784+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.14838v1","created_at":"2026-05-17T23:38:57.972784+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14838","created_at":"2026-05-17T23:38:57.972784+00:00"},{"alias_kind":"pith_short_12","alias_value":"FYAADENTEFOJ","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"FYAADENTEFOJGJLZ","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"FYAADENT","created_at":"2026-05-18T12:33:37.589309+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/FYAADENTEFOJGJLZQCXBMNXHIX","json":"https://pith.science/pith/FYAADENTEFOJGJLZQCXBMNXHIX.json","graph_json":"https://pith.science/api/pith-number/FYAADENTEFOJGJLZQCXBMNXHIX/graph.json","events_json":"https://pith.science/api/pith-number/FYAADENTEFOJGJLZQCXBMNXHIX/events.json","paper":"https://pith.science/paper/FYAADENT"},"agent_actions":{"view_html":"https://pith.science/pith/FYAADENTEFOJGJLZQCXBMNXHIX","download_json":"https://pith.science/pith/FYAADENTEFOJGJLZQCXBMNXHIX.json","view_paper":"https://pith.science/paper/FYAADENT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.14838&json=true","fetch_graph":"https://pith.science/api/pith-number/FYAADENTEFOJGJLZQCXBMNXHIX/graph.json","fetch_events":"https://pith.science/api/pith-number/FYAADENTEFOJGJLZQCXBMNXHIX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FYAADENTEFOJGJLZQCXBMNXHIX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FYAADENTEFOJGJLZQCXBMNXHIX/action/storage_attestation","attest_author":"https://pith.science/pith/FYAADENTEFOJGJLZQCXBMNXHIX/action/author_attestation","sign_citation":"https://pith.science/pith/FYAADENTEFOJGJLZQCXBMNXHIX/action/citation_signature","submit_replication":"https://pith.science/pith/FYAADENTEFOJGJLZQCXBMNXHIX/action/replication_record"}},"created_at":"2026-05-17T23:38:57.972784+00:00","updated_at":"2026-05-17T23:38:57.972784+00:00"}