{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:NATG7OQKRJDPEFX3JXO3QDWFUI","short_pith_number":"pith:NATG7OQK","canonical_record":{"source":{"id":"1609.03536","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-12T19:13:46Z","cross_cats_sorted":[],"title_canon_sha256":"2cdea16175190748ca44bafdd587be4a3c99560ee4eeab02bd5415588f5f7c07","abstract_canon_sha256":"29a6e2989b26895cbf303829cbed35fe312eebd60966d40acb19e1f9c3025d0c"},"schema_version":"1.0"},"canonical_sha256":"68266fba0a8a46f216fb4dddb80ec5a20453d72ccc79a8f73b3fb90fe1d70594","source":{"kind":"arxiv","id":"1609.03536","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.03536","created_at":"2026-05-18T01:04:46Z"},{"alias_kind":"arxiv_version","alias_value":"1609.03536v1","created_at":"2026-05-18T01:04:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.03536","created_at":"2026-05-18T01:04:46Z"},{"alias_kind":"pith_short_12","alias_value":"NATG7OQKRJDP","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_16","alias_value":"NATG7OQKRJDPEFX3","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_8","alias_value":"NATG7OQK","created_at":"2026-05-18T12:30:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:NATG7OQKRJDPEFX3JXO3QDWFUI","target":"record","payload":{"canonical_record":{"source":{"id":"1609.03536","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-12T19:13:46Z","cross_cats_sorted":[],"title_canon_sha256":"2cdea16175190748ca44bafdd587be4a3c99560ee4eeab02bd5415588f5f7c07","abstract_canon_sha256":"29a6e2989b26895cbf303829cbed35fe312eebd60966d40acb19e1f9c3025d0c"},"schema_version":"1.0"},"canonical_sha256":"68266fba0a8a46f216fb4dddb80ec5a20453d72ccc79a8f73b3fb90fe1d70594","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:46.730424Z","signature_b64":"k0lh2VRy+KMfpOm8uGPqUCHO0Z3LxGkDKhztRz4DUqcHjRzMpxMigPEARhrAnWdm/T/yFirqrpNk23HpiOFuCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68266fba0a8a46f216fb4dddb80ec5a20453d72ccc79a8f73b3fb90fe1d70594","last_reissued_at":"2026-05-18T01:04:46.729894Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:46.729894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1609.03536","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":"2026-05-18T01:04:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZMnE4NgGXRwK1VJLjQYToFLs/WoozZ3PArFWOjjSfHOX08Dp3JsHbbkhBPTCsH96RC7lH2p9oGnSOrYUHLfbDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T16:52:40.501582Z"},"content_sha256":"e8861cd686fed0297d81824294ea7c2bbf0d902f730226924255d6811a896925","schema_version":"1.0","event_id":"sha256:e8861cd686fed0297d81824294ea7c2bbf0d902f730226924255d6811a896925"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:NATG7OQKRJDPEFX3JXO3QDWFUI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Multi-Scale Cascade Fully Convolutional Network Face Detector","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ram Nevatia, Zhenheng Yang","submitted_at":"2016-09-12T19:13:46Z","abstract_excerpt":"Face detection is challenging as faces in images could be present at arbitrary locations and in different scales. We propose a three-stage cascade structure based on fully convolutional neural networks (FCNs). It first proposes the approximate locations where the faces may be, then aims to find the accurate location by zooming on to the faces. Each level of the FCN cascade is a multi-scale fully-convolutional network, which generates scores at different locations and in different scales. A score map is generated after each FCN stage. Probable regions of face are selected and fed to the next st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.03536","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":"2026-05-18T01:04:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A0UJfZ4sc6uzXES6xxBO6F9FXkA96yYMt6s7IuadDUMeTD7R4e8av23LnIbI6BC6K+D2LmJLJlZ4Qwa3bOn+BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T16:52:40.501937Z"},"content_sha256":"239dd53c83883d73368c21f517b570ba97be6444f4b5bcefd23f493d209891fa","schema_version":"1.0","event_id":"sha256:239dd53c83883d73368c21f517b570ba97be6444f4b5bcefd23f493d209891fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NATG7OQKRJDPEFX3JXO3QDWFUI/bundle.json","state_url":"https://pith.science/pith/NATG7OQKRJDPEFX3JXO3QDWFUI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NATG7OQKRJDPEFX3JXO3QDWFUI/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":"2026-06-11T16:52:40Z","links":{"resolver":"https://pith.science/pith/NATG7OQKRJDPEFX3JXO3QDWFUI","bundle":"https://pith.science/pith/NATG7OQKRJDPEFX3JXO3QDWFUI/bundle.json","state":"https://pith.science/pith/NATG7OQKRJDPEFX3JXO3QDWFUI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NATG7OQKRJDPEFX3JXO3QDWFUI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:NATG7OQKRJDPEFX3JXO3QDWFUI","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":"29a6e2989b26895cbf303829cbed35fe312eebd60966d40acb19e1f9c3025d0c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-12T19:13:46Z","title_canon_sha256":"2cdea16175190748ca44bafdd587be4a3c99560ee4eeab02bd5415588f5f7c07"},"schema_version":"1.0","source":{"id":"1609.03536","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.03536","created_at":"2026-05-18T01:04:46Z"},{"alias_kind":"arxiv_version","alias_value":"1609.03536v1","created_at":"2026-05-18T01:04:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.03536","created_at":"2026-05-18T01:04:46Z"},{"alias_kind":"pith_short_12","alias_value":"NATG7OQKRJDP","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_16","alias_value":"NATG7OQKRJDPEFX3","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_8","alias_value":"NATG7OQK","created_at":"2026-05-18T12:30:32Z"}],"graph_snapshots":[{"event_id":"sha256:239dd53c83883d73368c21f517b570ba97be6444f4b5bcefd23f493d209891fa","target":"graph","created_at":"2026-05-18T01:04:46Z","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":"Face detection is challenging as faces in images could be present at arbitrary locations and in different scales. We propose a three-stage cascade structure based on fully convolutional neural networks (FCNs). It first proposes the approximate locations where the faces may be, then aims to find the accurate location by zooming on to the faces. Each level of the FCN cascade is a multi-scale fully-convolutional network, which generates scores at different locations and in different scales. A score map is generated after each FCN stage. Probable regions of face are selected and fed to the next st","authors_text":"Ram Nevatia, Zhenheng Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-12T19:13:46Z","title":"A Multi-Scale Cascade Fully Convolutional Network Face Detector"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.03536","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:e8861cd686fed0297d81824294ea7c2bbf0d902f730226924255d6811a896925","target":"record","created_at":"2026-05-18T01:04:46Z","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":"29a6e2989b26895cbf303829cbed35fe312eebd60966d40acb19e1f9c3025d0c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-12T19:13:46Z","title_canon_sha256":"2cdea16175190748ca44bafdd587be4a3c99560ee4eeab02bd5415588f5f7c07"},"schema_version":"1.0","source":{"id":"1609.03536","kind":"arxiv","version":1}},"canonical_sha256":"68266fba0a8a46f216fb4dddb80ec5a20453d72ccc79a8f73b3fb90fe1d70594","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"68266fba0a8a46f216fb4dddb80ec5a20453d72ccc79a8f73b3fb90fe1d70594","first_computed_at":"2026-05-18T01:04:46.729894Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:04:46.729894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k0lh2VRy+KMfpOm8uGPqUCHO0Z3LxGkDKhztRz4DUqcHjRzMpxMigPEARhrAnWdm/T/yFirqrpNk23HpiOFuCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:04:46.730424Z","signed_message":"canonical_sha256_bytes"},"source_id":"1609.03536","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e8861cd686fed0297d81824294ea7c2bbf0d902f730226924255d6811a896925","sha256:239dd53c83883d73368c21f517b570ba97be6444f4b5bcefd23f493d209891fa"],"state_sha256":"3ef819c00091835db0d6152187592cb563fba75e870288809001a6b6812a77f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fyTHVvn76bv4smxPzrJa+7G35Hi2UyHlA18xioAW8pW3xOr2onFeF7aLAWmw++ORwEQ0qxk5oTOzfR9w7SmsAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T16:52:40.504087Z","bundle_sha256":"ba2f359b7f139fff92d3e4169877f60dd1ee073711b840ced211bf0a921283ee"}}