{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OOI55A65JOO5FCJ2IIKJNWZDXS","short_pith_number":"pith:OOI55A65","schema_version":"1.0","canonical_sha256":"7391de83dd4b9dd2893a421496db23bca1fba611b6d3c2c636442f945c94f63d","source":{"kind":"arxiv","id":"1811.07468","version":1},"attestation_state":"computed","paper":{"title":"Multi-scale 3D Convolution Network for Video Based Person Re-Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianing Li, Shiliang Zhang, Tiejun Huang","submitted_at":"2018-11-19T02:40:32Z","abstract_excerpt":"This paper proposes a two-stream convolution network to extract spatial and temporal cues for video based person Re-Identification (ReID). A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network. The resulting M3D convolution network introduces a fraction of parameters into the 2D CNN, but gains the ability of multi-scale temporal feature learning. With this compact architecture, M3D convolution network is also more efficient and easier to optimize than existing 3D convolution networks. The temporal stream further invo"},"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":"1811.07468","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T02:40:32Z","cross_cats_sorted":[],"title_canon_sha256":"8389ec8ece3089783829518c408487a3a06de589046024ecbd78098e15071309","abstract_canon_sha256":"dca4f9975150596c2d3c2f28db78bd76024e82462474a15b45c2e4c41988dded"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:24.415126Z","signature_b64":"tc9Q9+yRbzFqmWtC0Y5OcmV5EM8Jc3a94viy+sC5vLf2RyiSwcZos2RyEjPL0P5xAJ9AnW61UFbAf/S3pWZYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7391de83dd4b9dd2893a421496db23bca1fba611b6d3c2c636442f945c94f63d","last_reissued_at":"2026-05-18T00:00:24.414560Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:24.414560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-scale 3D Convolution Network for Video Based Person Re-Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianing Li, Shiliang Zhang, Tiejun Huang","submitted_at":"2018-11-19T02:40:32Z","abstract_excerpt":"This paper proposes a two-stream convolution network to extract spatial and temporal cues for video based person Re-Identification (ReID). A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network. The resulting M3D convolution network introduces a fraction of parameters into the 2D CNN, but gains the ability of multi-scale temporal feature learning. With this compact architecture, M3D convolution network is also more efficient and easier to optimize than existing 3D convolution networks. The temporal stream further invo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.07468","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":"1811.07468","created_at":"2026-05-18T00:00:24.414661+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.07468v1","created_at":"2026-05-18T00:00:24.414661+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07468","created_at":"2026-05-18T00:00:24.414661+00:00"},{"alias_kind":"pith_short_12","alias_value":"OOI55A65JOO5","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OOI55A65JOO5FCJ2","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OOI55A65","created_at":"2026-05-18T12:32:43.782077+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/OOI55A65JOO5FCJ2IIKJNWZDXS","json":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS.json","graph_json":"https://pith.science/api/pith-number/OOI55A65JOO5FCJ2IIKJNWZDXS/graph.json","events_json":"https://pith.science/api/pith-number/OOI55A65JOO5FCJ2IIKJNWZDXS/events.json","paper":"https://pith.science/paper/OOI55A65"},"agent_actions":{"view_html":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS","download_json":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS.json","view_paper":"https://pith.science/paper/OOI55A65","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.07468&json=true","fetch_graph":"https://pith.science/api/pith-number/OOI55A65JOO5FCJ2IIKJNWZDXS/graph.json","fetch_events":"https://pith.science/api/pith-number/OOI55A65JOO5FCJ2IIKJNWZDXS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/action/storage_attestation","attest_author":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/action/author_attestation","sign_citation":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/action/citation_signature","submit_replication":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/action/replication_record"}},"created_at":"2026-05-18T00:00:24.414661+00:00","updated_at":"2026-05-18T00:00:24.414661+00:00"}