{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LTDXMHRL6CCUJMEOZB2VU5WH77","short_pith_number":"pith:LTDXMHRL","schema_version":"1.0","canonical_sha256":"5cc7761e2bf08544b08ec8755a76c7ffee411dc6cfd2a4b9bd3bcae14c16ab90","source":{"kind":"arxiv","id":"1707.01408","version":3},"attestation_state":"computed","paper":{"title":"Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander G. Hauptmann, Lu Jiang, Po-Yao Huang, Ye Yuan, Zhenzhong Lan","submitted_at":"2017-07-05T14:15:06Z","abstract_excerpt":"We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge. In this multi-label video classification task, our pipeline achieved 84.675% and 84.662% GAP on our evaluation split and the official test set. We attribute the good performance to three components: 1) Refined video representation learning with residual links and hypercolumns 2) Latent concept mining which captures interactions among concepts. 3) Learning with temporal segments and weighted multi-model ensemble. We conduct experiments to validate and analyze the contribution of our models"},"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":"1707.01408","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-05T14:15:06Z","cross_cats_sorted":[],"title_canon_sha256":"194072a5110ed655424eff6983f21444000c029f2a398d3a065d3dc876d5cf10","abstract_canon_sha256":"4114615bf00e5747eba30a3f010ac98e4ef7d1cc5aa6eb9fe133b942955c98a9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:31.049635Z","signature_b64":"ssRO8P65HuxJBEifTsnP6yfEObdNc7Ap74f83rHuAJ01itwmKZE0twIqF/mYgedN3cDkgQdpm4zgWZHSJN+CAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cc7761e2bf08544b08ec8755a76c7ffee411dc6cfd2a4b9bd3bcae14c16ab90","last_reissued_at":"2026-05-18T00:39:31.048880Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:31.048880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander G. Hauptmann, Lu Jiang, Po-Yao Huang, Ye Yuan, Zhenzhong Lan","submitted_at":"2017-07-05T14:15:06Z","abstract_excerpt":"We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge. In this multi-label video classification task, our pipeline achieved 84.675% and 84.662% GAP on our evaluation split and the official test set. We attribute the good performance to three components: 1) Refined video representation learning with residual links and hypercolumns 2) Latent concept mining which captures interactions among concepts. 3) Learning with temporal segments and weighted multi-model ensemble. We conduct experiments to validate and analyze the contribution of our models"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.01408","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":""},"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":"1707.01408","created_at":"2026-05-18T00:39:31.049014+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.01408v3","created_at":"2026-05-18T00:39:31.049014+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.01408","created_at":"2026-05-18T00:39:31.049014+00:00"},{"alias_kind":"pith_short_12","alias_value":"LTDXMHRL6CCU","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LTDXMHRL6CCUJMEO","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LTDXMHRL","created_at":"2026-05-18T12:31:28.150371+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/LTDXMHRL6CCUJMEOZB2VU5WH77","json":"https://pith.science/pith/LTDXMHRL6CCUJMEOZB2VU5WH77.json","graph_json":"https://pith.science/api/pith-number/LTDXMHRL6CCUJMEOZB2VU5WH77/graph.json","events_json":"https://pith.science/api/pith-number/LTDXMHRL6CCUJMEOZB2VU5WH77/events.json","paper":"https://pith.science/paper/LTDXMHRL"},"agent_actions":{"view_html":"https://pith.science/pith/LTDXMHRL6CCUJMEOZB2VU5WH77","download_json":"https://pith.science/pith/LTDXMHRL6CCUJMEOZB2VU5WH77.json","view_paper":"https://pith.science/paper/LTDXMHRL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.01408&json=true","fetch_graph":"https://pith.science/api/pith-number/LTDXMHRL6CCUJMEOZB2VU5WH77/graph.json","fetch_events":"https://pith.science/api/pith-number/LTDXMHRL6CCUJMEOZB2VU5WH77/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LTDXMHRL6CCUJMEOZB2VU5WH77/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LTDXMHRL6CCUJMEOZB2VU5WH77/action/storage_attestation","attest_author":"https://pith.science/pith/LTDXMHRL6CCUJMEOZB2VU5WH77/action/author_attestation","sign_citation":"https://pith.science/pith/LTDXMHRL6CCUJMEOZB2VU5WH77/action/citation_signature","submit_replication":"https://pith.science/pith/LTDXMHRL6CCUJMEOZB2VU5WH77/action/replication_record"}},"created_at":"2026-05-18T00:39:31.049014+00:00","updated_at":"2026-05-18T00:39:31.049014+00:00"}