{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OOI55A65JOO5FCJ2IIKJNWZDXS","short_pith_number":"pith:OOI55A65","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"},"canonical_sha256":"7391de83dd4b9dd2893a421496db23bca1fba611b6d3c2c636442f945c94f63d","source":{"kind":"arxiv","id":"1811.07468","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.07468","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"arxiv_version","alias_value":"1811.07468v1","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07468","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"pith_short_12","alias_value":"OOI55A65JOO5","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OOI55A65JOO5FCJ2","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OOI55A65","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OOI55A65JOO5FCJ2IIKJNWZDXS","target":"record","payload":{"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"},"canonical_sha256":"7391de83dd4b9dd2893a421496db23bca1fba611b6d3c2c636442f945c94f63d","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"},"source_kind":"arxiv","source_id":"1811.07468","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:00:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+5ZArYndWGnNntNkL/xEKvBkFje22oK3nLr7IYzlqNaGVW9bZkPrYWBc06JYO3Njh2FMbvZoxI2oXS8EcnKRCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T04:22:38.781119Z"},"content_sha256":"6e66348469b5f6ef804bcd47d295e665e51e8d067475f1ae32d08c29d92830bf","schema_version":"1.0","event_id":"sha256:6e66348469b5f6ef804bcd47d295e665e51e8d067475f1ae32d08c29d92830bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OOI55A65JOO5FCJ2IIKJNWZDXS","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:00:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0JfmitCj8fwmhR6NtnPl9Nevo7QZPf7WGX6VQIo9PX9c8kXbG/QqpX0JUPfW3/ZE49VGhahQTmnQVJUXIYP2CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T04:22:38.781493Z"},"content_sha256":"bb11f84d3603cecfe165560b680960cc6411d52cdb5deeed292a14c083d08e62","schema_version":"1.0","event_id":"sha256:bb11f84d3603cecfe165560b680960cc6411d52cdb5deeed292a14c083d08e62"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/bundle.json","state_url":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-27T04:22:38Z","links":{"resolver":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS","bundle":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/bundle.json","state":"https://pith.science/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OOI55A65JOO5FCJ2IIKJNWZDXS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OOI55A65JOO5FCJ2IIKJNWZDXS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"dca4f9975150596c2d3c2f28db78bd76024e82462474a15b45c2e4c41988dded","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T02:40:32Z","title_canon_sha256":"8389ec8ece3089783829518c408487a3a06de589046024ecbd78098e15071309"},"schema_version":"1.0","source":{"id":"1811.07468","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.07468","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"arxiv_version","alias_value":"1811.07468v1","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07468","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"pith_short_12","alias_value":"OOI55A65JOO5","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OOI55A65JOO5FCJ2","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OOI55A65","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:bb11f84d3603cecfe165560b680960cc6411d52cdb5deeed292a14c083d08e62","target":"graph","created_at":"2026-05-18T00:00:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"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","authors_text":"Jianing Li, Shiliang Zhang, Tiejun Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T02:40:32Z","title":"Multi-scale 3D Convolution Network for Video Based Person Re-Identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.07468","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6e66348469b5f6ef804bcd47d295e665e51e8d067475f1ae32d08c29d92830bf","target":"record","created_at":"2026-05-18T00:00:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"dca4f9975150596c2d3c2f28db78bd76024e82462474a15b45c2e4c41988dded","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T02:40:32Z","title_canon_sha256":"8389ec8ece3089783829518c408487a3a06de589046024ecbd78098e15071309"},"schema_version":"1.0","source":{"id":"1811.07468","kind":"arxiv","version":1}},"canonical_sha256":"7391de83dd4b9dd2893a421496db23bca1fba611b6d3c2c636442f945c94f63d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7391de83dd4b9dd2893a421496db23bca1fba611b6d3c2c636442f945c94f63d","first_computed_at":"2026-05-18T00:00:24.414560Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:24.414560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tc9Q9+yRbzFqmWtC0Y5OcmV5EM8Jc3a94viy+sC5vLf2RyiSwcZos2RyEjPL0P5xAJ9AnW61UFbAf/S3pWZYAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:24.415126Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.07468","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6e66348469b5f6ef804bcd47d295e665e51e8d067475f1ae32d08c29d92830bf","sha256:bb11f84d3603cecfe165560b680960cc6411d52cdb5deeed292a14c083d08e62"],"state_sha256":"91a0477703c9fac8fb1578814a60d7a423bcf7fcb590ce3915fd8e197e645a96"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NcSww1Ddv8SfvujwNXK49n3SQOWZl19hcqPgRKJxmaSmm+eU6sUeRhqzMkZvWjA55dxOMgsiAKuA8WpNUHBiAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T04:22:38.783710Z","bundle_sha256":"bb4e626494e597ee4113e67e37af039f3a664e47bf7718abdd41720cc2ab101e"}}