{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:BRPR3YCAEZBKHJSZ4476QI4EUB","short_pith_number":"pith:BRPR3YCA","schema_version":"1.0","canonical_sha256":"0c5f1de0402642a3a659e73fe82384a04a9b191cfab203e1a58130693fdb9f36","source":{"kind":"arxiv","id":"1505.00315","version":1},"attestation_state":"computed","paper":{"title":"Learning Temporal Embeddings for Complex Video Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Greg Mori, Kevin Tang, Li Fei-Fei, Vignesh Ramanathan","submitted_at":"2015-05-02T06:43:28Z","abstract_excerpt":"In this paper, we propose to learn temporal embeddings of video frames for complex video analysis. Large quantities of unlabeled video data can be easily obtained from the Internet. These videos possess the implicit weak label that they are sequences of temporally and semantically coherent images. We leverage this information to learn temporal embeddings for video frames by associating frames with the temporal context that they appear in. To do this, we propose a scheme for incorporating temporal context based on past and future frames in videos, and compare this to other contextual representa"},"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":"1505.00315","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-05-02T06:43:28Z","cross_cats_sorted":[],"title_canon_sha256":"5c48a6423d3a0bf3512ea72d785eeca8e4a8a5de920f3582b641f15f4ea7e666","abstract_canon_sha256":"48cc9c37bb6297dcc51223af5c69b70754d1b0d5cb4960cf6fb4f5c99d7be240"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:17:10.484016Z","signature_b64":"p5zGwUVRaIlvIqINz9aEhba2CRHYv/dgczHmKiEPwNOoWEwHPkVpadIbUI9aIRruTAG6ON68HlXYqSaho+n+BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c5f1de0402642a3a659e73fe82384a04a9b191cfab203e1a58130693fdb9f36","last_reissued_at":"2026-05-18T02:17:10.483328Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:17:10.483328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Temporal Embeddings for Complex Video Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Greg Mori, Kevin Tang, Li Fei-Fei, Vignesh Ramanathan","submitted_at":"2015-05-02T06:43:28Z","abstract_excerpt":"In this paper, we propose to learn temporal embeddings of video frames for complex video analysis. Large quantities of unlabeled video data can be easily obtained from the Internet. These videos possess the implicit weak label that they are sequences of temporally and semantically coherent images. We leverage this information to learn temporal embeddings for video frames by associating frames with the temporal context that they appear in. To do this, we propose a scheme for incorporating temporal context based on past and future frames in videos, and compare this to other contextual representa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.00315","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":"1505.00315","created_at":"2026-05-18T02:17:10.483452+00:00"},{"alias_kind":"arxiv_version","alias_value":"1505.00315v1","created_at":"2026-05-18T02:17:10.483452+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.00315","created_at":"2026-05-18T02:17:10.483452+00:00"},{"alias_kind":"pith_short_12","alias_value":"BRPR3YCAEZBK","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_16","alias_value":"BRPR3YCAEZBKHJSZ","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_8","alias_value":"BRPR3YCA","created_at":"2026-05-18T12:29:14.074870+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/BRPR3YCAEZBKHJSZ4476QI4EUB","json":"https://pith.science/pith/BRPR3YCAEZBKHJSZ4476QI4EUB.json","graph_json":"https://pith.science/api/pith-number/BRPR3YCAEZBKHJSZ4476QI4EUB/graph.json","events_json":"https://pith.science/api/pith-number/BRPR3YCAEZBKHJSZ4476QI4EUB/events.json","paper":"https://pith.science/paper/BRPR3YCA"},"agent_actions":{"view_html":"https://pith.science/pith/BRPR3YCAEZBKHJSZ4476QI4EUB","download_json":"https://pith.science/pith/BRPR3YCAEZBKHJSZ4476QI4EUB.json","view_paper":"https://pith.science/paper/BRPR3YCA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1505.00315&json=true","fetch_graph":"https://pith.science/api/pith-number/BRPR3YCAEZBKHJSZ4476QI4EUB/graph.json","fetch_events":"https://pith.science/api/pith-number/BRPR3YCAEZBKHJSZ4476QI4EUB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BRPR3YCAEZBKHJSZ4476QI4EUB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BRPR3YCAEZBKHJSZ4476QI4EUB/action/storage_attestation","attest_author":"https://pith.science/pith/BRPR3YCAEZBKHJSZ4476QI4EUB/action/author_attestation","sign_citation":"https://pith.science/pith/BRPR3YCAEZBKHJSZ4476QI4EUB/action/citation_signature","submit_replication":"https://pith.science/pith/BRPR3YCAEZBKHJSZ4476QI4EUB/action/replication_record"}},"created_at":"2026-05-18T02:17:10.483452+00:00","updated_at":"2026-05-18T02:17:10.483452+00:00"}