{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:PVSVEW7JXEOCJO7DULC5AFRIZH","short_pith_number":"pith:PVSVEW7J","canonical_record":{"source":{"id":"1807.05511","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-15T08:16:03Z","cross_cats_sorted":[],"title_canon_sha256":"d90d401c6c7556a4cbe1b2261f76387674519c1be42b96a81ff4c0d241198e5c","abstract_canon_sha256":"e916ffa0e9664da315585a7b057ccc1213a3311c83f0b0d17f5f29dbe27e27dd"},"schema_version":"1.0"},"canonical_sha256":"7d65525be9b91c24bbe3a2c5d01628c9e4add5a6890d64774eececf7ce35f320","source":{"kind":"arxiv","id":"1807.05511","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.05511","created_at":"2026-05-17T23:48:34Z"},{"alias_kind":"arxiv_version","alias_value":"1807.05511v2","created_at":"2026-05-17T23:48:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.05511","created_at":"2026-05-17T23:48:34Z"},{"alias_kind":"pith_short_12","alias_value":"PVSVEW7JXEOC","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"PVSVEW7JXEOCJO7D","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"PVSVEW7J","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:PVSVEW7JXEOCJO7DULC5AFRIZH","target":"record","payload":{"canonical_record":{"source":{"id":"1807.05511","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-15T08:16:03Z","cross_cats_sorted":[],"title_canon_sha256":"d90d401c6c7556a4cbe1b2261f76387674519c1be42b96a81ff4c0d241198e5c","abstract_canon_sha256":"e916ffa0e9664da315585a7b057ccc1213a3311c83f0b0d17f5f29dbe27e27dd"},"schema_version":"1.0"},"canonical_sha256":"7d65525be9b91c24bbe3a2c5d01628c9e4add5a6890d64774eececf7ce35f320","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:34.036199Z","signature_b64":"rrJXcwEdygNdCZrCyXSrfp/u9N/kKXnCAEFNVj5KXHE587D5Z6E0kVe9MFIRuHeykkjD2eg2nq1sXWEXR/J8Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d65525be9b91c24bbe3a2c5d01628c9e4add5a6890d64774eececf7ce35f320","last_reissued_at":"2026-05-17T23:48:34.035719Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:34.035719Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.05511","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":"2026-05-17T23:48:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5rhC9fe9YXNy+3wDk4a/oDAmBoJ2Gjwcr0LMLHMaodVq1Ly0qHqls1nOzveSwFB3tEMoBYJeEt8SvXalfBgQAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T09:43:05.840119Z"},"content_sha256":"fd507af6a5ef574822f1510aa01bd411108e8705dcaf3051107ef7fbe997eb26","schema_version":"1.0","event_id":"sha256:fd507af6a5ef574822f1510aa01bd411108e8705dcaf3051107ef7fbe997eb26"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:PVSVEW7JXEOCJO7DULC5AFRIZH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Object Detection with Deep Learning: A Review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Peng Zheng, Shou-tao Xu, Xindong Wu, Zhong-Qiu Zhao","submitted_at":"2018-07-15T08:16:03Z","abstract_excerpt":"Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.05511","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":"2026-05-17T23:48:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E+o/CBTjR2LV1s5k/X9mtIPQlJKDwrmJDtnozlCWuiyDtw42JzwOq8EZKEUNjjTdW9oamBNdNC4NO8sy1fA/Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T09:43:05.840806Z"},"content_sha256":"aefa0b46af7e6678d92d07a54eabfa33cde714ba46e8cfc3a228641b209f755c","schema_version":"1.0","event_id":"sha256:aefa0b46af7e6678d92d07a54eabfa33cde714ba46e8cfc3a228641b209f755c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PVSVEW7JXEOCJO7DULC5AFRIZH/bundle.json","state_url":"https://pith.science/pith/PVSVEW7JXEOCJO7DULC5AFRIZH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PVSVEW7JXEOCJO7DULC5AFRIZH/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":"2026-05-26T09:43:05Z","links":{"resolver":"https://pith.science/pith/PVSVEW7JXEOCJO7DULC5AFRIZH","bundle":"https://pith.science/pith/PVSVEW7JXEOCJO7DULC5AFRIZH/bundle.json","state":"https://pith.science/pith/PVSVEW7JXEOCJO7DULC5AFRIZH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PVSVEW7JXEOCJO7DULC5AFRIZH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:PVSVEW7JXEOCJO7DULC5AFRIZH","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":"e916ffa0e9664da315585a7b057ccc1213a3311c83f0b0d17f5f29dbe27e27dd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-15T08:16:03Z","title_canon_sha256":"d90d401c6c7556a4cbe1b2261f76387674519c1be42b96a81ff4c0d241198e5c"},"schema_version":"1.0","source":{"id":"1807.05511","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.05511","created_at":"2026-05-17T23:48:34Z"},{"alias_kind":"arxiv_version","alias_value":"1807.05511v2","created_at":"2026-05-17T23:48:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.05511","created_at":"2026-05-17T23:48:34Z"},{"alias_kind":"pith_short_12","alias_value":"PVSVEW7JXEOC","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"PVSVEW7JXEOCJO7D","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"PVSVEW7J","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:aefa0b46af7e6678d92d07a54eabfa33cde714ba46e8cfc3a228641b209f755c","target":"graph","created_at":"2026-05-17T23:48:34Z","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":"Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the","authors_text":"Peng Zheng, Shou-tao Xu, Xindong Wu, Zhong-Qiu Zhao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-15T08:16:03Z","title":"Object Detection with Deep Learning: A Review"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.05511","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:fd507af6a5ef574822f1510aa01bd411108e8705dcaf3051107ef7fbe997eb26","target":"record","created_at":"2026-05-17T23:48:34Z","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":"e916ffa0e9664da315585a7b057ccc1213a3311c83f0b0d17f5f29dbe27e27dd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-15T08:16:03Z","title_canon_sha256":"d90d401c6c7556a4cbe1b2261f76387674519c1be42b96a81ff4c0d241198e5c"},"schema_version":"1.0","source":{"id":"1807.05511","kind":"arxiv","version":2}},"canonical_sha256":"7d65525be9b91c24bbe3a2c5d01628c9e4add5a6890d64774eececf7ce35f320","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7d65525be9b91c24bbe3a2c5d01628c9e4add5a6890d64774eececf7ce35f320","first_computed_at":"2026-05-17T23:48:34.035719Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:34.035719Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rrJXcwEdygNdCZrCyXSrfp/u9N/kKXnCAEFNVj5KXHE587D5Z6E0kVe9MFIRuHeykkjD2eg2nq1sXWEXR/J8Cw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:34.036199Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.05511","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd507af6a5ef574822f1510aa01bd411108e8705dcaf3051107ef7fbe997eb26","sha256:aefa0b46af7e6678d92d07a54eabfa33cde714ba46e8cfc3a228641b209f755c"],"state_sha256":"4e49068c9336b772a090f9cb034cc812691c99cbab2489e47b62b2d19c735063"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3Co/M7Wd+eRer3901KECjhTAMai5Y/y7JKPWokAoS+XnjhS6roggdS1ob6O5BLYvouJAgThIu3PULrS1K5alCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T09:43:05.844099Z","bundle_sha256":"47289ab473c69089c02e0d4087abb082abc3bc481b100fec21b87374009945c8"}}