{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:SSS54WUORCW7SFFVDC32XGV2UJ","short_pith_number":"pith:SSS54WUO","canonical_record":{"source":{"id":"1511.07247","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-23T14:51:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"36347f0dba88ce9a713f9552d12bf9f3b895f932a04bb77dc6c3e53e879f63f9","abstract_canon_sha256":"7dc939eeb1bc0f896f66689f48019f2af75a75f6d07b4295f1e6204e4e1dd535"},"schema_version":"1.0"},"canonical_sha256":"94a5de5a8e88adf914b518b7ab9abaa26da3d6e7561a59e9e79e6f93a126d527","source":{"kind":"arxiv","id":"1511.07247","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.07247","created_at":"2026-05-18T01:15:57Z"},{"alias_kind":"arxiv_version","alias_value":"1511.07247v3","created_at":"2026-05-18T01:15:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.07247","created_at":"2026-05-18T01:15:57Z"},{"alias_kind":"pith_short_12","alias_value":"SSS54WUORCW7","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"SSS54WUORCW7SFFV","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"SSS54WUO","created_at":"2026-05-18T12:29:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:SSS54WUORCW7SFFVDC32XGV2UJ","target":"record","payload":{"canonical_record":{"source":{"id":"1511.07247","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-23T14:51:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"36347f0dba88ce9a713f9552d12bf9f3b895f932a04bb77dc6c3e53e879f63f9","abstract_canon_sha256":"7dc939eeb1bc0f896f66689f48019f2af75a75f6d07b4295f1e6204e4e1dd535"},"schema_version":"1.0"},"canonical_sha256":"94a5de5a8e88adf914b518b7ab9abaa26da3d6e7561a59e9e79e6f93a126d527","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:57.902392Z","signature_b64":"omNzoaO8ecyumB2SlpF6zb4OK3B6iVxTyluYUVD7vScNMX4sJ90rgYkxQyqa1NTkc5kepN9HqYmDuGW7846eBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94a5de5a8e88adf914b518b7ab9abaa26da3d6e7561a59e9e79e6f93a126d527","last_reissued_at":"2026-05-18T01:15:57.901708Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:57.901708Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.07247","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:15:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Aihi0is59MtgCo8rJ9jUsWs6euq4s+xZ50JZaoLr+49BuU7zw3MX1uE4i0Jh13ES5TWXQttPZ9PBWUs++I5MAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T07:00:15.058048Z"},"content_sha256":"516e0cdaa19529ce0f6cafd1325985b5f96a9ab6b07fd168f9635abe7a386fd3","schema_version":"1.0","event_id":"sha256:516e0cdaa19529ce0f6cafd1325985b5f96a9ab6b07fd168f9635abe7a386fd3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:SSS54WUORCW7SFFVDC32XGV2UJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NetVLAD: CNN architecture for weakly supervised place recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Akihiko Torii, Josef Sivic, Petr Gronat, Relja Arandjelovi\\'c, Tomas Pajdla","submitted_at":"2015-11-23T14:51:51Z","abstract_excerpt":"We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following three principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the \"Vector of Locally Aggregated Descriptors\" image representation commonly used in image retrieval. The layer is readily pluggable into a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.07247","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:15:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S0rfEh93AJrsOHjJQqziHZIa5CO6RMl/5y7sVRPk7u/MfAkHw4J8ut4Q0VbaBNyQm6E9f59HfBaLgxUatPLAAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T07:00:15.058431Z"},"content_sha256":"f6a941200d3727f6aa72cd2d3d8051030264d1f65bfcff8dd57eb196dc0b3d21","schema_version":"1.0","event_id":"sha256:f6a941200d3727f6aa72cd2d3d8051030264d1f65bfcff8dd57eb196dc0b3d21"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SSS54WUORCW7SFFVDC32XGV2UJ/bundle.json","state_url":"https://pith.science/pith/SSS54WUORCW7SFFVDC32XGV2UJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SSS54WUORCW7SFFVDC32XGV2UJ/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":"2026-05-23T07:00:15Z","links":{"resolver":"https://pith.science/pith/SSS54WUORCW7SFFVDC32XGV2UJ","bundle":"https://pith.science/pith/SSS54WUORCW7SFFVDC32XGV2UJ/bundle.json","state":"https://pith.science/pith/SSS54WUORCW7SFFVDC32XGV2UJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SSS54WUORCW7SFFVDC32XGV2UJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:SSS54WUORCW7SFFVDC32XGV2UJ","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":"7dc939eeb1bc0f896f66689f48019f2af75a75f6d07b4295f1e6204e4e1dd535","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-23T14:51:51Z","title_canon_sha256":"36347f0dba88ce9a713f9552d12bf9f3b895f932a04bb77dc6c3e53e879f63f9"},"schema_version":"1.0","source":{"id":"1511.07247","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.07247","created_at":"2026-05-18T01:15:57Z"},{"alias_kind":"arxiv_version","alias_value":"1511.07247v3","created_at":"2026-05-18T01:15:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.07247","created_at":"2026-05-18T01:15:57Z"},{"alias_kind":"pith_short_12","alias_value":"SSS54WUORCW7","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"SSS54WUORCW7SFFV","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"SSS54WUO","created_at":"2026-05-18T12:29:42Z"}],"graph_snapshots":[{"event_id":"sha256:f6a941200d3727f6aa72cd2d3d8051030264d1f65bfcff8dd57eb196dc0b3d21","target":"graph","created_at":"2026-05-18T01:15:57Z","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":"We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following three principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the \"Vector of Locally Aggregated Descriptors\" image representation commonly used in image retrieval. The layer is readily pluggable into a","authors_text":"Akihiko Torii, Josef Sivic, Petr Gronat, Relja Arandjelovi\\'c, Tomas Pajdla","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-23T14:51:51Z","title":"NetVLAD: CNN architecture for weakly supervised place recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.07247","kind":"arxiv","version":3},"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":"sha256:516e0cdaa19529ce0f6cafd1325985b5f96a9ab6b07fd168f9635abe7a386fd3","target":"record","created_at":"2026-05-18T01:15:57Z","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":"7dc939eeb1bc0f896f66689f48019f2af75a75f6d07b4295f1e6204e4e1dd535","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-23T14:51:51Z","title_canon_sha256":"36347f0dba88ce9a713f9552d12bf9f3b895f932a04bb77dc6c3e53e879f63f9"},"schema_version":"1.0","source":{"id":"1511.07247","kind":"arxiv","version":3}},"canonical_sha256":"94a5de5a8e88adf914b518b7ab9abaa26da3d6e7561a59e9e79e6f93a126d527","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"94a5de5a8e88adf914b518b7ab9abaa26da3d6e7561a59e9e79e6f93a126d527","first_computed_at":"2026-05-18T01:15:57.901708Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:15:57.901708Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"omNzoaO8ecyumB2SlpF6zb4OK3B6iVxTyluYUVD7vScNMX4sJ90rgYkxQyqa1NTkc5kepN9HqYmDuGW7846eBA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:15:57.902392Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.07247","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:516e0cdaa19529ce0f6cafd1325985b5f96a9ab6b07fd168f9635abe7a386fd3","sha256:f6a941200d3727f6aa72cd2d3d8051030264d1f65bfcff8dd57eb196dc0b3d21"],"state_sha256":"306436840e7b9e39fe1d7309c55e6d46fce3597bebee63ba25d39eb1fe78b8bf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i9NPOMoS38i5g+wTGWErYDJ+O2z3fuhPQPkwU0aLZUFCnZOyrH77STtHH9VzuXTZP46sov7s/HXvzQzaALzkAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T07:00:15.060843Z","bundle_sha256":"7f9dd809396c1d0bfe9cb5719f53f224a9466f2f118579cf46c320edb4820d36"}}