{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:JZNET3XP5ZGVIQI62YZX66LNVS","short_pith_number":"pith:JZNET3XP","schema_version":"1.0","canonical_sha256":"4e5a49eeefee4d54411ed6337f796dac891c22baf96418b8f1533828ff64f936","source":{"kind":"arxiv","id":"1910.06961","version":3},"attestation_state":"computed","paper":{"title":"Tiny Video Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo","submitted_at":"2019-10-15T17:55:37Z","abstract_excerpt":"Video understanding is a challenging problem with great impact on the abilities of autonomous agents working in the real-world. Yet, solutions so far have been computationally intensive, with the fastest algorithms running for more than half a second per video snippet on powerful GPUs. We propose a novel idea on video architecture learning - Tiny Video Networks - which automatically designs highly efficient models for video understanding. The tiny video models run with competitive performance for as low as 37 milliseconds per video on a CPU and 10 milliseconds on a standard GPU."},"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":"1910.06961","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-10-15T17:55:37Z","cross_cats_sorted":[],"title_canon_sha256":"e8a881fc1faf7ed5e14751a94910adedd1924a20d7301600cda3401d962f87a1","abstract_canon_sha256":"165ae985cdf5ba49e2a30a859ba945ecbaff1688bbb6a733a15bf42e17816ae8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:53:50.395977Z","signature_b64":"KgB6cdMJk+VoVjd7DRfCHaFg6fuZ8aWs9W5xxmAl/szkG+xuZCgU8NFH+s227HcP9UvfW+AN5CI++JkBHAslAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e5a49eeefee4d54411ed6337f796dac891c22baf96418b8f1533828ff64f936","last_reissued_at":"2026-07-05T02:53:50.395604Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:53:50.395604Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tiny Video Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo","submitted_at":"2019-10-15T17:55:37Z","abstract_excerpt":"Video understanding is a challenging problem with great impact on the abilities of autonomous agents working in the real-world. Yet, solutions so far have been computationally intensive, with the fastest algorithms running for more than half a second per video snippet on powerful GPUs. We propose a novel idea on video architecture learning - Tiny Video Networks - which automatically designs highly efficient models for video understanding. The tiny video models run with competitive performance for as low as 37 milliseconds per video on a CPU and 10 milliseconds on a standard GPU."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.06961","kind":"arxiv","version":3},"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/1910.06961/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":"1910.06961","created_at":"2026-07-05T02:53:50.395652+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.06961v3","created_at":"2026-07-05T02:53:50.395652+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.06961","created_at":"2026-07-05T02:53:50.395652+00:00"},{"alias_kind":"pith_short_12","alias_value":"JZNET3XP5ZGV","created_at":"2026-07-05T02:53:50.395652+00:00"},{"alias_kind":"pith_short_16","alias_value":"JZNET3XP5ZGVIQI6","created_at":"2026-07-05T02:53:50.395652+00:00"},{"alias_kind":"pith_short_8","alias_value":"JZNET3XP","created_at":"2026-07-05T02:53:50.395652+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.14495","citing_title":"BILLNET: A Binarized Conv3D-LSTM Network with Logic-gated residual architecture for hardware-efficient video inference","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JZNET3XP5ZGVIQI62YZX66LNVS","json":"https://pith.science/pith/JZNET3XP5ZGVIQI62YZX66LNVS.json","graph_json":"https://pith.science/api/pith-number/JZNET3XP5ZGVIQI62YZX66LNVS/graph.json","events_json":"https://pith.science/api/pith-number/JZNET3XP5ZGVIQI62YZX66LNVS/events.json","paper":"https://pith.science/paper/JZNET3XP"},"agent_actions":{"view_html":"https://pith.science/pith/JZNET3XP5ZGVIQI62YZX66LNVS","download_json":"https://pith.science/pith/JZNET3XP5ZGVIQI62YZX66LNVS.json","view_paper":"https://pith.science/paper/JZNET3XP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.06961&json=true","fetch_graph":"https://pith.science/api/pith-number/JZNET3XP5ZGVIQI62YZX66LNVS/graph.json","fetch_events":"https://pith.science/api/pith-number/JZNET3XP5ZGVIQI62YZX66LNVS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JZNET3XP5ZGVIQI62YZX66LNVS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JZNET3XP5ZGVIQI62YZX66LNVS/action/storage_attestation","attest_author":"https://pith.science/pith/JZNET3XP5ZGVIQI62YZX66LNVS/action/author_attestation","sign_citation":"https://pith.science/pith/JZNET3XP5ZGVIQI62YZX66LNVS/action/citation_signature","submit_replication":"https://pith.science/pith/JZNET3XP5ZGVIQI62YZX66LNVS/action/replication_record"}},"created_at":"2026-07-05T02:53:50.395652+00:00","updated_at":"2026-07-05T02:53:50.395652+00:00"}