{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:QZGNUHLUUIFLXRDXXE7UOOYUBB","short_pith_number":"pith:QZGNUHLU","schema_version":"1.0","canonical_sha256":"864cda1d74a20abbc477b93f473b14087b292d23e203695b825f3008ca494088","source":{"kind":"arxiv","id":"1608.03609","version":1},"attestation_state":"computed","paper":{"title":"Clockwork Convnets for Video Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Evan Shelhamer, Judy Hoffman, Kate Rakelly, Trevor Darrell","submitted_at":"2016-08-11T20:32:55Z","abstract_excerpt":"Recent years have seen tremendous progress in still-image segmentation; however the na\\\"ive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: 1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and 2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of \"clockwork\" conv"},"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":"1608.03609","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-11T20:32:55Z","cross_cats_sorted":[],"title_canon_sha256":"584f3d122e453ad7ea48f8ba51781423bb7e12bbaf4aa462d0582a49336f7137","abstract_canon_sha256":"4562daca3387697f39600c08f0cd211a5a47170fa0322d83bc1370d7ff8cee71"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:09:12.405021Z","signature_b64":"GCUV5X9nievTw4tUnS727oMv6aW3JnqFOCZNBU+UdZoqzbHkvEa1kqveYoK4TcNqo3eMkcSz9RVZPhDB2X9ABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"864cda1d74a20abbc477b93f473b14087b292d23e203695b825f3008ca494088","last_reissued_at":"2026-05-18T01:09:12.404505Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:09:12.404505Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Clockwork Convnets for Video Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Evan Shelhamer, Judy Hoffman, Kate Rakelly, Trevor Darrell","submitted_at":"2016-08-11T20:32:55Z","abstract_excerpt":"Recent years have seen tremendous progress in still-image segmentation; however the na\\\"ive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: 1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and 2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of \"clockwork\" conv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.03609","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":"1608.03609","created_at":"2026-05-18T01:09:12.404595+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.03609v1","created_at":"2026-05-18T01:09:12.404595+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.03609","created_at":"2026-05-18T01:09:12.404595+00:00"},{"alias_kind":"pith_short_12","alias_value":"QZGNUHLUUIFL","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"QZGNUHLUUIFLXRDX","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"QZGNUHLU","created_at":"2026-05-18T12:30:41.710351+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/QZGNUHLUUIFLXRDXXE7UOOYUBB","json":"https://pith.science/pith/QZGNUHLUUIFLXRDXXE7UOOYUBB.json","graph_json":"https://pith.science/api/pith-number/QZGNUHLUUIFLXRDXXE7UOOYUBB/graph.json","events_json":"https://pith.science/api/pith-number/QZGNUHLUUIFLXRDXXE7UOOYUBB/events.json","paper":"https://pith.science/paper/QZGNUHLU"},"agent_actions":{"view_html":"https://pith.science/pith/QZGNUHLUUIFLXRDXXE7UOOYUBB","download_json":"https://pith.science/pith/QZGNUHLUUIFLXRDXXE7UOOYUBB.json","view_paper":"https://pith.science/paper/QZGNUHLU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.03609&json=true","fetch_graph":"https://pith.science/api/pith-number/QZGNUHLUUIFLXRDXXE7UOOYUBB/graph.json","fetch_events":"https://pith.science/api/pith-number/QZGNUHLUUIFLXRDXXE7UOOYUBB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QZGNUHLUUIFLXRDXXE7UOOYUBB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QZGNUHLUUIFLXRDXXE7UOOYUBB/action/storage_attestation","attest_author":"https://pith.science/pith/QZGNUHLUUIFLXRDXXE7UOOYUBB/action/author_attestation","sign_citation":"https://pith.science/pith/QZGNUHLUUIFLXRDXXE7UOOYUBB/action/citation_signature","submit_replication":"https://pith.science/pith/QZGNUHLUUIFLXRDXXE7UOOYUBB/action/replication_record"}},"created_at":"2026-05-18T01:09:12.404595+00:00","updated_at":"2026-05-18T01:09:12.404595+00:00"}