{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:G7EIL2RDOZGUNPC2FNSKEZC7E4","short_pith_number":"pith:G7EIL2RD","canonical_record":{"source":{"id":"1711.02512","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-03T21:29:55Z","cross_cats_sorted":[],"title_canon_sha256":"90eb9dc8ac317f766b40e2ca0d27c78e65f15eec8c9c0fc80030029c2cd2b192","abstract_canon_sha256":"c878d8bbb3c6023192ca95b978857ee6fc6afac44f7f2c02c80f8b311924640e"},"schema_version":"1.0"},"canonical_sha256":"37c885ea23764d46bc5a2b64a2645f272fd7b0f5c7130068d592d8d916deb71b","source":{"kind":"arxiv","id":"1711.02512","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.02512","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"arxiv_version","alias_value":"1711.02512v2","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.02512","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"pith_short_12","alias_value":"G7EIL2RDOZGU","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"G7EIL2RDOZGUNPC2","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"G7EIL2RD","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:G7EIL2RDOZGUNPC2FNSKEZC7E4","target":"record","payload":{"canonical_record":{"source":{"id":"1711.02512","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-03T21:29:55Z","cross_cats_sorted":[],"title_canon_sha256":"90eb9dc8ac317f766b40e2ca0d27c78e65f15eec8c9c0fc80030029c2cd2b192","abstract_canon_sha256":"c878d8bbb3c6023192ca95b978857ee6fc6afac44f7f2c02c80f8b311924640e"},"schema_version":"1.0"},"canonical_sha256":"37c885ea23764d46bc5a2b64a2645f272fd7b0f5c7130068d592d8d916deb71b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:00.287784Z","signature_b64":"f+p9jE+sU//2PqatMZfpLS7NddRur8r03ufL17D0YSnQ45n00ZvdjCzfr7h4FYMPfJ/bBboVu4tDfMRuqfnfDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37c885ea23764d46bc5a2b64a2645f272fd7b0f5c7130068d592d8d916deb71b","last_reissued_at":"2026-05-18T00:11:00.287055Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:00.287055Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.02512","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:11:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H6z4DFf4QXn3JbBCQdBDDoQ1jUcqAgLA/30g5m0pRPSwynXIxPaENPncOcKG+MVRBfW+EvK5G02WnMG4u4MIAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T19:11:30.169846Z"},"content_sha256":"b92474db17687de1bd06c8992b9fad32ed1d9bf1101f8bccb9f779a8dbb0feec","schema_version":"1.0","event_id":"sha256:b92474db17687de1bd06c8992b9fad32ed1d9bf1101f8bccb9f779a8dbb0feec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:G7EIL2RDOZGUNPC2FNSKEZC7E4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fine-tuning CNN Image Retrieval with No Human Annotation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Filip Radenovi\\'c, Giorgos Tolias, Ond\\v{r}ej Chum","submitted_at":"2017-11-03T21:29:55Z","abstract_excerpt":"Image descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of representation, and search efficiency. Training of CNNs, either from scratch or fine-tuning, requires a large amount of annotated data, where a high quality of annotation is often crucial. In this work, we propose to fine-tune CNNs for image retrieval on a large collection of unordered images in a fully automated manner. Reconstructed 3D models obtained by the state-of-the-art retrieval and structure-from-motion methods guide t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.02512","kind":"arxiv","version":2},"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":"2026-05-18T00:11:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zTQr4DP8C3Agwj7NnGbsa2dWdoAnYii7KZbWpUlbDftXSfQ8btBVeapNfj8ZM0INM/0sHXlOZ0cTlMtE/VdvDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T19:11:30.170194Z"},"content_sha256":"e0129db46eead77e0804f7dcab2dc9ec6982078a1961e998ea3d464169139daf","schema_version":"1.0","event_id":"sha256:e0129db46eead77e0804f7dcab2dc9ec6982078a1961e998ea3d464169139daf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G7EIL2RDOZGUNPC2FNSKEZC7E4/bundle.json","state_url":"https://pith.science/pith/G7EIL2RDOZGUNPC2FNSKEZC7E4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G7EIL2RDOZGUNPC2FNSKEZC7E4/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":"2026-05-28T19:11:30Z","links":{"resolver":"https://pith.science/pith/G7EIL2RDOZGUNPC2FNSKEZC7E4","bundle":"https://pith.science/pith/G7EIL2RDOZGUNPC2FNSKEZC7E4/bundle.json","state":"https://pith.science/pith/G7EIL2RDOZGUNPC2FNSKEZC7E4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G7EIL2RDOZGUNPC2FNSKEZC7E4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:G7EIL2RDOZGUNPC2FNSKEZC7E4","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":"c878d8bbb3c6023192ca95b978857ee6fc6afac44f7f2c02c80f8b311924640e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-03T21:29:55Z","title_canon_sha256":"90eb9dc8ac317f766b40e2ca0d27c78e65f15eec8c9c0fc80030029c2cd2b192"},"schema_version":"1.0","source":{"id":"1711.02512","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.02512","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"arxiv_version","alias_value":"1711.02512v2","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.02512","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"pith_short_12","alias_value":"G7EIL2RDOZGU","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"G7EIL2RDOZGUNPC2","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"G7EIL2RD","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:e0129db46eead77e0804f7dcab2dc9ec6982078a1961e998ea3d464169139daf","target":"graph","created_at":"2026-05-18T00:11:00Z","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":"Image descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of representation, and search efficiency. Training of CNNs, either from scratch or fine-tuning, requires a large amount of annotated data, where a high quality of annotation is often crucial. In this work, we propose to fine-tune CNNs for image retrieval on a large collection of unordered images in a fully automated manner. Reconstructed 3D models obtained by the state-of-the-art retrieval and structure-from-motion methods guide t","authors_text":"Filip Radenovi\\'c, Giorgos Tolias, Ond\\v{r}ej Chum","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-03T21:29:55Z","title":"Fine-tuning CNN Image Retrieval with No Human Annotation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.02512","kind":"arxiv","version":2},"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:b92474db17687de1bd06c8992b9fad32ed1d9bf1101f8bccb9f779a8dbb0feec","target":"record","created_at":"2026-05-18T00:11:00Z","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":"c878d8bbb3c6023192ca95b978857ee6fc6afac44f7f2c02c80f8b311924640e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-03T21:29:55Z","title_canon_sha256":"90eb9dc8ac317f766b40e2ca0d27c78e65f15eec8c9c0fc80030029c2cd2b192"},"schema_version":"1.0","source":{"id":"1711.02512","kind":"arxiv","version":2}},"canonical_sha256":"37c885ea23764d46bc5a2b64a2645f272fd7b0f5c7130068d592d8d916deb71b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37c885ea23764d46bc5a2b64a2645f272fd7b0f5c7130068d592d8d916deb71b","first_computed_at":"2026-05-18T00:11:00.287055Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:00.287055Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"f+p9jE+sU//2PqatMZfpLS7NddRur8r03ufL17D0YSnQ45n00ZvdjCzfr7h4FYMPfJ/bBboVu4tDfMRuqfnfDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:00.287784Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.02512","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b92474db17687de1bd06c8992b9fad32ed1d9bf1101f8bccb9f779a8dbb0feec","sha256:e0129db46eead77e0804f7dcab2dc9ec6982078a1961e998ea3d464169139daf"],"state_sha256":"9a71c31a7414ce25939180b2b2b3340b27607435fe17c2c04306303308ce15a8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5alMUk5gaiIEosZ6AON7MOy/2vcF0XzhNW4mxSZm6DT08WX2R+YrYOG+hpVzlnqPxemznoC5BDCQgINzCVRYBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T19:11:30.172467Z","bundle_sha256":"4e5c5c4b3333a81d9370c5fa3d8e79579def85b5d47625eec142a3d76bfc6a10"}}