{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RRE2ZHLS2AX4LGDAB2XTPR4JQR","short_pith_number":"pith:RRE2ZHLS","schema_version":"1.0","canonical_sha256":"8c49ac9d72d02fc598600eaf37c789845bd77efc86b35f38f736a7f9c3884cca","source":{"kind":"arxiv","id":"2107.00239","version":1},"attestation_state":"computed","paper":{"title":"Generic Event Boundary Detection Challenge at CVPR 2021 Technical Report: Cascaded Temporal Attention Network (CASTANET)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Congcong Li, Dexiang Hong, Libo Zhang, Longyin Wen, Xinyao Wang","submitted_at":"2021-07-01T06:37:01Z","abstract_excerpt":"This report presents the approach used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR21. In this work, we design a Cascaded Temporal Attention Network (CASTANET) for GEBD, which is formed by three parts, the backbone network, the temporal attention module, and the classification module. Specifically, the Channel-Separated Convolutional Network (CSN) is used as the backbone network to extract features, and the temporal attention module is designed to enforce the network to focus on the discriminative features. After that, the cascaded architecture is used in the "},"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":"2107.00239","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-01T06:37:01Z","cross_cats_sorted":[],"title_canon_sha256":"597c68b8f8ae0356f445d89c3f48ca3a2cf6c2348754d827df7104f73105f57f","abstract_canon_sha256":"0da54793b650e4f5ead043cb1affe6bc8878ea0b4f86479bf65fa3ae13a1fb85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:54:22.485282Z","signature_b64":"nW8KtRZyQS6whO4GXIK0O7WauyOyvAHKxaJ6UkTCHlRWUr6AoAMR6IPyGzt5/6Toqt115ed4562fnmatjcU8CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c49ac9d72d02fc598600eaf37c789845bd77efc86b35f38f736a7f9c3884cca","last_reissued_at":"2026-07-05T02:54:22.484868Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:54:22.484868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generic Event Boundary Detection Challenge at CVPR 2021 Technical Report: Cascaded Temporal Attention Network (CASTANET)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Congcong Li, Dexiang Hong, Libo Zhang, Longyin Wen, Xinyao Wang","submitted_at":"2021-07-01T06:37:01Z","abstract_excerpt":"This report presents the approach used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR21. In this work, we design a Cascaded Temporal Attention Network (CASTANET) for GEBD, which is formed by three parts, the backbone network, the temporal attention module, and the classification module. Specifically, the Channel-Separated Convolutional Network (CSN) is used as the backbone network to extract features, and the temporal attention module is designed to enforce the network to focus on the discriminative features. After that, the cascaded architecture is used in the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.00239","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/2107.00239/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":"2107.00239","created_at":"2026-07-05T02:54:22.484928+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.00239v1","created_at":"2026-07-05T02:54:22.484928+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.00239","created_at":"2026-07-05T02:54:22.484928+00:00"},{"alias_kind":"pith_short_12","alias_value":"RRE2ZHLS2AX4","created_at":"2026-07-05T02:54:22.484928+00:00"},{"alias_kind":"pith_short_16","alias_value":"RRE2ZHLS2AX4LGDA","created_at":"2026-07-05T02:54:22.484928+00:00"},{"alias_kind":"pith_short_8","alias_value":"RRE2ZHLS","created_at":"2026-07-05T02:54:22.484928+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/RRE2ZHLS2AX4LGDAB2XTPR4JQR","json":"https://pith.science/pith/RRE2ZHLS2AX4LGDAB2XTPR4JQR.json","graph_json":"https://pith.science/api/pith-number/RRE2ZHLS2AX4LGDAB2XTPR4JQR/graph.json","events_json":"https://pith.science/api/pith-number/RRE2ZHLS2AX4LGDAB2XTPR4JQR/events.json","paper":"https://pith.science/paper/RRE2ZHLS"},"agent_actions":{"view_html":"https://pith.science/pith/RRE2ZHLS2AX4LGDAB2XTPR4JQR","download_json":"https://pith.science/pith/RRE2ZHLS2AX4LGDAB2XTPR4JQR.json","view_paper":"https://pith.science/paper/RRE2ZHLS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.00239&json=true","fetch_graph":"https://pith.science/api/pith-number/RRE2ZHLS2AX4LGDAB2XTPR4JQR/graph.json","fetch_events":"https://pith.science/api/pith-number/RRE2ZHLS2AX4LGDAB2XTPR4JQR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RRE2ZHLS2AX4LGDAB2XTPR4JQR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RRE2ZHLS2AX4LGDAB2XTPR4JQR/action/storage_attestation","attest_author":"https://pith.science/pith/RRE2ZHLS2AX4LGDAB2XTPR4JQR/action/author_attestation","sign_citation":"https://pith.science/pith/RRE2ZHLS2AX4LGDAB2XTPR4JQR/action/citation_signature","submit_replication":"https://pith.science/pith/RRE2ZHLS2AX4LGDAB2XTPR4JQR/action/replication_record"}},"created_at":"2026-07-05T02:54:22.484928+00:00","updated_at":"2026-07-05T02:54:22.484928+00:00"}