{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OSETMACFUV6JNR63IMGGRPUIU3","short_pith_number":"pith:OSETMACF","schema_version":"1.0","canonical_sha256":"7489360045a57c96c7db430c68be88a6eea0dcf16a164ef1c164742730f1ae5a","source":{"kind":"arxiv","id":"1703.09145","version":1},"attestation_state":"computed","paper":{"title":"Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained \"Hard Faces\"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Martin D. Levine, Yuguang Liu","submitted_at":"2017-03-27T15:31:00Z","abstract_excerpt":"Large-scale variations still pose a challenge in unconstrained face detection. To the best of our knowledge, no current face detection algorithm can detect a face as large as 800 x 800 pixels while simultaneously detecting another one as small as 8 x 8 pixels within a single image with equally high accuracy. We propose a two-stage cascaded face detection framework, Multi-Path Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a deep neural network with a classic learning strategy, to tackle this challenge. The first stage is a Multi-Path Region Proposal Network (MP-R"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1703.09145","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-27T15:31:00Z","cross_cats_sorted":[],"title_canon_sha256":"e92acd8c03f1c4ef6bbf9307b4bd2c4c4df04fc1917c475d27ebd0b8cba1e288","abstract_canon_sha256":"2956325f9af27369ec1dc0862fda6d4c4847bfb05c3ef9bdee6fdaf79dce6161"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:47:54.168138Z","signature_b64":"C4qLjkU6ve71ot74F7qsDDg3cMe8+gLGNvczwFtZYLDOTN03FUslA8NZV4JD0N/XFNO5yLvqySZ9XAcAU3cfCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7489360045a57c96c7db430c68be88a6eea0dcf16a164ef1c164742730f1ae5a","last_reissued_at":"2026-05-18T00:47:54.167567Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:47:54.167567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained \"Hard Faces\"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Martin D. Levine, Yuguang Liu","submitted_at":"2017-03-27T15:31:00Z","abstract_excerpt":"Large-scale variations still pose a challenge in unconstrained face detection. To the best of our knowledge, no current face detection algorithm can detect a face as large as 800 x 800 pixels while simultaneously detecting another one as small as 8 x 8 pixels within a single image with equally high accuracy. We propose a two-stage cascaded face detection framework, Multi-Path Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a deep neural network with a classic learning strategy, to tackle this challenge. The first stage is a Multi-Path Region Proposal Network (MP-R"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.09145","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1703.09145","created_at":"2026-05-18T00:47:54.167676+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.09145v1","created_at":"2026-05-18T00:47:54.167676+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.09145","created_at":"2026-05-18T00:47:54.167676+00:00"},{"alias_kind":"pith_short_12","alias_value":"OSETMACFUV6J","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OSETMACFUV6JNR63","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OSETMACF","created_at":"2026-05-18T12:31:34.259226+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OSETMACFUV6JNR63IMGGRPUIU3","json":"https://pith.science/pith/OSETMACFUV6JNR63IMGGRPUIU3.json","graph_json":"https://pith.science/api/pith-number/OSETMACFUV6JNR63IMGGRPUIU3/graph.json","events_json":"https://pith.science/api/pith-number/OSETMACFUV6JNR63IMGGRPUIU3/events.json","paper":"https://pith.science/paper/OSETMACF"},"agent_actions":{"view_html":"https://pith.science/pith/OSETMACFUV6JNR63IMGGRPUIU3","download_json":"https://pith.science/pith/OSETMACFUV6JNR63IMGGRPUIU3.json","view_paper":"https://pith.science/paper/OSETMACF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.09145&json=true","fetch_graph":"https://pith.science/api/pith-number/OSETMACFUV6JNR63IMGGRPUIU3/graph.json","fetch_events":"https://pith.science/api/pith-number/OSETMACFUV6JNR63IMGGRPUIU3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OSETMACFUV6JNR63IMGGRPUIU3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OSETMACFUV6JNR63IMGGRPUIU3/action/storage_attestation","attest_author":"https://pith.science/pith/OSETMACFUV6JNR63IMGGRPUIU3/action/author_attestation","sign_citation":"https://pith.science/pith/OSETMACFUV6JNR63IMGGRPUIU3/action/citation_signature","submit_replication":"https://pith.science/pith/OSETMACFUV6JNR63IMGGRPUIU3/action/replication_record"}},"created_at":"2026-05-18T00:47:54.167676+00:00","updated_at":"2026-05-18T00:47:54.167676+00:00"}