{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:7PFNKBJMZSQEJHFD6WF3WFK3UZ","short_pith_number":"pith:7PFNKBJM","schema_version":"1.0","canonical_sha256":"fbcad5052ccca0449ca3f58bbb155ba653110e98319fa3174ffb4882ee892365","source":{"kind":"arxiv","id":"1810.12522","version":1},"attestation_state":"computed","paper":{"title":"Random Temporal Skipping for Multirate Video Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CV","authors_text":"Shawn Newsam, Yi Zhu","submitted_at":"2018-10-30T04:35:43Z","abstract_excerpt":"Current state-of-the-art approaches to video understanding adopt temporal jittering to simulate analyzing the video at varying frame rates. However, this does not work well for multirate videos, in which actions or subactions occur at different speeds. The frame sampling rate should vary in accordance with the different motion speeds. In this work, we propose a simple yet effective strategy, termed random temporal skipping, to address this situation. This strategy effectively handles multirate videos by randomizing the sampling rate during training. It is an exhaustive approach, which can pote"},"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":"1810.12522","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-30T04:35:43Z","cross_cats_sorted":["cs.AI","cs.MM"],"title_canon_sha256":"4a6825da2484009f97f7ca213e78d1b15796f9d1dbfcf531cf3b223c6889decb","abstract_canon_sha256":"1663b03b99f820f45feb1cdda36925dda2c248c2ff8d441b76258a3b4abadc50"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:56.838873Z","signature_b64":"X6U/B1FC1N4rCnFNHuIGjg3ZpLg0ljWvgAdWNg3/xvtL9qYfDZhL6WzRCPJZThX4kxnWlK61YdkEtWZbaOWSAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbcad5052ccca0449ca3f58bbb155ba653110e98319fa3174ffb4882ee892365","last_reissued_at":"2026-05-18T00:01:56.838374Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:56.838374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Random Temporal Skipping for Multirate Video Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CV","authors_text":"Shawn Newsam, Yi Zhu","submitted_at":"2018-10-30T04:35:43Z","abstract_excerpt":"Current state-of-the-art approaches to video understanding adopt temporal jittering to simulate analyzing the video at varying frame rates. However, this does not work well for multirate videos, in which actions or subactions occur at different speeds. The frame sampling rate should vary in accordance with the different motion speeds. In this work, we propose a simple yet effective strategy, termed random temporal skipping, to address this situation. This strategy effectively handles multirate videos by randomizing the sampling rate during training. It is an exhaustive approach, which can pote"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12522","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":"1810.12522","created_at":"2026-05-18T00:01:56.838448+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.12522v1","created_at":"2026-05-18T00:01:56.838448+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12522","created_at":"2026-05-18T00:01:56.838448+00:00"},{"alias_kind":"pith_short_12","alias_value":"7PFNKBJMZSQE","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"7PFNKBJMZSQEJHFD","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"7PFNKBJM","created_at":"2026-05-18T12:32:11.075285+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/7PFNKBJMZSQEJHFD6WF3WFK3UZ","json":"https://pith.science/pith/7PFNKBJMZSQEJHFD6WF3WFK3UZ.json","graph_json":"https://pith.science/api/pith-number/7PFNKBJMZSQEJHFD6WF3WFK3UZ/graph.json","events_json":"https://pith.science/api/pith-number/7PFNKBJMZSQEJHFD6WF3WFK3UZ/events.json","paper":"https://pith.science/paper/7PFNKBJM"},"agent_actions":{"view_html":"https://pith.science/pith/7PFNKBJMZSQEJHFD6WF3WFK3UZ","download_json":"https://pith.science/pith/7PFNKBJMZSQEJHFD6WF3WFK3UZ.json","view_paper":"https://pith.science/paper/7PFNKBJM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.12522&json=true","fetch_graph":"https://pith.science/api/pith-number/7PFNKBJMZSQEJHFD6WF3WFK3UZ/graph.json","fetch_events":"https://pith.science/api/pith-number/7PFNKBJMZSQEJHFD6WF3WFK3UZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7PFNKBJMZSQEJHFD6WF3WFK3UZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7PFNKBJMZSQEJHFD6WF3WFK3UZ/action/storage_attestation","attest_author":"https://pith.science/pith/7PFNKBJMZSQEJHFD6WF3WFK3UZ/action/author_attestation","sign_citation":"https://pith.science/pith/7PFNKBJMZSQEJHFD6WF3WFK3UZ/action/citation_signature","submit_replication":"https://pith.science/pith/7PFNKBJMZSQEJHFD6WF3WFK3UZ/action/replication_record"}},"created_at":"2026-05-18T00:01:56.838448+00:00","updated_at":"2026-05-18T00:01:56.838448+00:00"}