{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:Q3GYX6IFI2NJLLVLMAICL46BOM","short_pith_number":"pith:Q3GYX6IF","canonical_record":{"source":{"id":"1806.02479","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-07T01:10:08Z","cross_cats_sorted":[],"title_canon_sha256":"42da547a50a1b254329f5c0bbe9e9487ad5f365363cb388e824e63d6a5eaa879","abstract_canon_sha256":"6d72d616b32f0045398f02d3977b1a4d8311c8e740bf7549f1b7fb8750fc875d"},"schema_version":"1.0"},"canonical_sha256":"86cd8bf905469a95aeab601025f3c173185a3a47ef973a461e0b16839ab55b57","source":{"kind":"arxiv","id":"1806.02479","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.02479","created_at":"2026-05-18T00:13:56Z"},{"alias_kind":"arxiv_version","alias_value":"1806.02479v1","created_at":"2026-05-18T00:13:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.02479","created_at":"2026-05-18T00:13:56Z"},{"alias_kind":"pith_short_12","alias_value":"Q3GYX6IFI2NJ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"Q3GYX6IFI2NJLLVL","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"Q3GYX6IF","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:Q3GYX6IFI2NJLLVLMAICL46BOM","target":"record","payload":{"canonical_record":{"source":{"id":"1806.02479","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-07T01:10:08Z","cross_cats_sorted":[],"title_canon_sha256":"42da547a50a1b254329f5c0bbe9e9487ad5f365363cb388e824e63d6a5eaa879","abstract_canon_sha256":"6d72d616b32f0045398f02d3977b1a4d8311c8e740bf7549f1b7fb8750fc875d"},"schema_version":"1.0"},"canonical_sha256":"86cd8bf905469a95aeab601025f3c173185a3a47ef973a461e0b16839ab55b57","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:56.709285Z","signature_b64":"GkUv2IvTcw97wW6s+n3gUjXy0u7ulwa6X3AFSrgBEk27aq/cs0Q1ZKvAF0xbrxUvFmdc3O4BR8i9bFkC+87GAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86cd8bf905469a95aeab601025f3c173185a3a47ef973a461e0b16839ab55b57","last_reissued_at":"2026-05-18T00:13:56.708606Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:56.708606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.02479","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":"2026-05-18T00:13:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8cs9wLL40p2lHzChd4p5ayeTihiiezjGtnpAYVpBWm5lDBkDCER6dH2KZVpmcElaW9KPqjLsrApv3xkPgyXDDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T06:11:38.097243Z"},"content_sha256":"8fa5015d76d90d632e650d662770439fa9a25f1c176467494629a63fdbbcfb58","schema_version":"1.0","event_id":"sha256:8fa5015d76d90d632e650d662770439fa9a25f1c176467494629a63fdbbcfb58"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:Q3GYX6IFI2NJLLVLMAICL46BOM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Interlinked Convolutional Neural Networks for Face Parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Zhang, Xiaolin Hu, Yisu Zhou","submitted_at":"2018-06-07T01:10:08Z","abstract_excerpt":"Face parsing is a basic task in face image analysis. It amounts to labeling each pixel with appropriate facial parts such as eyes and nose. In the paper, we present a interlinked convolutional neural network (iCNN) for solving this problem in an end-to-end fashion. It consists of multiple convolutional neural networks (CNNs) taking input in different scales. A special interlinking layer is designed to allow the CNNs to exchange information, enabling them to integrate local and contextual information efficiently. The hallmark of iCNN is the extensive use of downsampling and upsampling in the in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.02479","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":"2026-05-18T00:13:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SRyBhs3snBHaWS3ey5ugBjhaXPQQzTXXs+0IhR21R6WbElF90sO8T7Uw7OZBy5N1LkbyK+rloYQFkKzQSd7lCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T06:11:38.097612Z"},"content_sha256":"dea152bfa4bb94bf10ac59ed35dcdc2df693ac57756258cedbf710692465413e","schema_version":"1.0","event_id":"sha256:dea152bfa4bb94bf10ac59ed35dcdc2df693ac57756258cedbf710692465413e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q3GYX6IFI2NJLLVLMAICL46BOM/bundle.json","state_url":"https://pith.science/pith/Q3GYX6IFI2NJLLVLMAICL46BOM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q3GYX6IFI2NJLLVLMAICL46BOM/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":"2026-05-30T06:11:38Z","links":{"resolver":"https://pith.science/pith/Q3GYX6IFI2NJLLVLMAICL46BOM","bundle":"https://pith.science/pith/Q3GYX6IFI2NJLLVLMAICL46BOM/bundle.json","state":"https://pith.science/pith/Q3GYX6IFI2NJLLVLMAICL46BOM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q3GYX6IFI2NJLLVLMAICL46BOM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:Q3GYX6IFI2NJLLVLMAICL46BOM","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":"6d72d616b32f0045398f02d3977b1a4d8311c8e740bf7549f1b7fb8750fc875d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-07T01:10:08Z","title_canon_sha256":"42da547a50a1b254329f5c0bbe9e9487ad5f365363cb388e824e63d6a5eaa879"},"schema_version":"1.0","source":{"id":"1806.02479","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.02479","created_at":"2026-05-18T00:13:56Z"},{"alias_kind":"arxiv_version","alias_value":"1806.02479v1","created_at":"2026-05-18T00:13:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.02479","created_at":"2026-05-18T00:13:56Z"},{"alias_kind":"pith_short_12","alias_value":"Q3GYX6IFI2NJ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"Q3GYX6IFI2NJLLVL","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"Q3GYX6IF","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:dea152bfa4bb94bf10ac59ed35dcdc2df693ac57756258cedbf710692465413e","target":"graph","created_at":"2026-05-18T00:13:56Z","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 parsing is a basic task in face image analysis. It amounts to labeling each pixel with appropriate facial parts such as eyes and nose. In the paper, we present a interlinked convolutional neural network (iCNN) for solving this problem in an end-to-end fashion. It consists of multiple convolutional neural networks (CNNs) taking input in different scales. A special interlinking layer is designed to allow the CNNs to exchange information, enabling them to integrate local and contextual information efficiently. The hallmark of iCNN is the extensive use of downsampling and upsampling in the in","authors_text":"Bo Zhang, Xiaolin Hu, Yisu Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-07T01:10:08Z","title":"Interlinked Convolutional Neural Networks for Face Parsing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.02479","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":"sha256:8fa5015d76d90d632e650d662770439fa9a25f1c176467494629a63fdbbcfb58","target":"record","created_at":"2026-05-18T00:13:56Z","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":"6d72d616b32f0045398f02d3977b1a4d8311c8e740bf7549f1b7fb8750fc875d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-07T01:10:08Z","title_canon_sha256":"42da547a50a1b254329f5c0bbe9e9487ad5f365363cb388e824e63d6a5eaa879"},"schema_version":"1.0","source":{"id":"1806.02479","kind":"arxiv","version":1}},"canonical_sha256":"86cd8bf905469a95aeab601025f3c173185a3a47ef973a461e0b16839ab55b57","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86cd8bf905469a95aeab601025f3c173185a3a47ef973a461e0b16839ab55b57","first_computed_at":"2026-05-18T00:13:56.708606Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:56.708606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GkUv2IvTcw97wW6s+n3gUjXy0u7ulwa6X3AFSrgBEk27aq/cs0Q1ZKvAF0xbrxUvFmdc3O4BR8i9bFkC+87GAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:56.709285Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.02479","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8fa5015d76d90d632e650d662770439fa9a25f1c176467494629a63fdbbcfb58","sha256:dea152bfa4bb94bf10ac59ed35dcdc2df693ac57756258cedbf710692465413e"],"state_sha256":"8b0e80d03eaed8970637b2154f07861bc62168df30e21eeb00468683f3bfb455"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aaIfyl4AP/J7/Mr8nUXDUPecKvonbnE+uusXnRSlRHT0Tm9+3icQWSsyTX8wkILUnO/LsYnu0yFJof6HFklDDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T06:11:38.100017Z","bundle_sha256":"ba84592f8f937d4d2f1e159c52b5eca49aabe95389fd794c1fdd6bcd0ae6da12"}}