{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:DLWNG5PHWWFNM74TLFWSCGPSG5","short_pith_number":"pith:DLWNG5PH","canonical_record":{"source":{"id":"2212.08423","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-16T11:52:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"250afa5f776e77384fb638ca415aee817591bf2fa0b1641cb70e51bd9ae04d59","abstract_canon_sha256":"4b7fa66f12cdf8b12175c27ec77f894d90ff97fdd24a8b94cf8f70f9584c48fb"},"schema_version":"1.0"},"canonical_sha256":"1aecd375e7b58ad67f93596d2119f237515c98a85c29fca9404fabaa99923926","source":{"kind":"arxiv","id":"2212.08423","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.08423","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"arxiv_version","alias_value":"2212.08423v1","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.08423","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"pith_short_12","alias_value":"DLWNG5PHWWFN","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"pith_short_16","alias_value":"DLWNG5PHWWFNM74T","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"pith_short_8","alias_value":"DLWNG5PH","created_at":"2026-07-05T05:25:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:DLWNG5PHWWFNM74TLFWSCGPSG5","target":"record","payload":{"canonical_record":{"source":{"id":"2212.08423","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-16T11:52:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"250afa5f776e77384fb638ca415aee817591bf2fa0b1641cb70e51bd9ae04d59","abstract_canon_sha256":"4b7fa66f12cdf8b12175c27ec77f894d90ff97fdd24a8b94cf8f70f9584c48fb"},"schema_version":"1.0"},"canonical_sha256":"1aecd375e7b58ad67f93596d2119f237515c98a85c29fca9404fabaa99923926","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:25:57.116860Z","signature_b64":"u9utHZW5w1cN7xItLDwijignYdor13aZXUkMaPXHouQMmMuf3YLKM74QU11VBDCRGwY+mNQKpl2CnL7UJPm/Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1aecd375e7b58ad67f93596d2119f237515c98a85c29fca9404fabaa99923926","last_reissued_at":"2026-07-05T05:25:57.116390Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:25:57.116390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.08423","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":"2026-07-05T05:25:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0oUujqH9EDtsHb5gjl7Ug3C8rW47zEE24fDJV/P87ynNZtNawKi39XlSIfaPw9KlJkH0O4i0EnR7xB6CRoVwDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:12:17.818923Z"},"content_sha256":"77dc1611487197166ca7fadaceb0678d2359d49e5d055226b0be42efedbcb275","schema_version":"1.0","event_id":"sha256:77dc1611487197166ca7fadaceb0678d2359d49e5d055226b0be42efedbcb275"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:DLWNG5PHWWFNM74TLFWSCGPSG5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Context Label Learning: Improving Background Class Representations in Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ben Glocker, Chen Chen, Cheng Ouyang, Konstantinos Kamnitsas, Zeju Li","submitted_at":"2022-12-16T11:52:15Z","abstract_excerpt":"Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensitivity and precision. The issue concerns the highly heterogeneous nature of the background class, resulting in multi-modal distributions. Empirically, we find that neural networks trained with heterogeneous background struggle to map the corresponding contextual samples to compact clusters in feature space. As a result, the distribution over backgr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.08423","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2212.08423/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T05:25:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Anbm1ath3cAazI/sED0wQJ25fMMwGm9EeqCbe9CJocXMIbTlqz752h9WgixOPMv9TU8gH/OC13/XWCFTjX//Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:12:17.819425Z"},"content_sha256":"308eaa3adc830a0fbab5c823afea495ec060b5c873a7a9dd115510ca2beead1c","schema_version":"1.0","event_id":"sha256:308eaa3adc830a0fbab5c823afea495ec060b5c873a7a9dd115510ca2beead1c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DLWNG5PHWWFNM74TLFWSCGPSG5/bundle.json","state_url":"https://pith.science/pith/DLWNG5PHWWFNM74TLFWSCGPSG5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DLWNG5PHWWFNM74TLFWSCGPSG5/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":"2026-08-03T19:12:17Z","links":{"resolver":"https://pith.science/pith/DLWNG5PHWWFNM74TLFWSCGPSG5","bundle":"https://pith.science/pith/DLWNG5PHWWFNM74TLFWSCGPSG5/bundle.json","state":"https://pith.science/pith/DLWNG5PHWWFNM74TLFWSCGPSG5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DLWNG5PHWWFNM74TLFWSCGPSG5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:DLWNG5PHWWFNM74TLFWSCGPSG5","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":"4b7fa66f12cdf8b12175c27ec77f894d90ff97fdd24a8b94cf8f70f9584c48fb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-16T11:52:15Z","title_canon_sha256":"250afa5f776e77384fb638ca415aee817591bf2fa0b1641cb70e51bd9ae04d59"},"schema_version":"1.0","source":{"id":"2212.08423","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.08423","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"arxiv_version","alias_value":"2212.08423v1","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.08423","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"pith_short_12","alias_value":"DLWNG5PHWWFN","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"pith_short_16","alias_value":"DLWNG5PHWWFNM74T","created_at":"2026-07-05T05:25:57Z"},{"alias_kind":"pith_short_8","alias_value":"DLWNG5PH","created_at":"2026-07-05T05:25:57Z"}],"graph_snapshots":[{"event_id":"sha256:308eaa3adc830a0fbab5c823afea495ec060b5c873a7a9dd115510ca2beead1c","target":"graph","created_at":"2026-07-05T05:25:57Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2212.08423/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensitivity and precision. The issue concerns the highly heterogeneous nature of the background class, resulting in multi-modal distributions. Empirically, we find that neural networks trained with heterogeneous background struggle to map the corresponding contextual samples to compact clusters in feature space. As a result, the distribution over backgr","authors_text":"Ben Glocker, Chen Chen, Cheng Ouyang, Konstantinos Kamnitsas, Zeju Li","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-16T11:52:15Z","title":"Context Label Learning: Improving Background Class Representations in Semantic Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.08423","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":"sha256:77dc1611487197166ca7fadaceb0678d2359d49e5d055226b0be42efedbcb275","target":"record","created_at":"2026-07-05T05:25:57Z","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":"4b7fa66f12cdf8b12175c27ec77f894d90ff97fdd24a8b94cf8f70f9584c48fb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-16T11:52:15Z","title_canon_sha256":"250afa5f776e77384fb638ca415aee817591bf2fa0b1641cb70e51bd9ae04d59"},"schema_version":"1.0","source":{"id":"2212.08423","kind":"arxiv","version":1}},"canonical_sha256":"1aecd375e7b58ad67f93596d2119f237515c98a85c29fca9404fabaa99923926","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1aecd375e7b58ad67f93596d2119f237515c98a85c29fca9404fabaa99923926","first_computed_at":"2026-07-05T05:25:57.116390Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:25:57.116390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u9utHZW5w1cN7xItLDwijignYdor13aZXUkMaPXHouQMmMuf3YLKM74QU11VBDCRGwY+mNQKpl2CnL7UJPm/Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:25:57.116860Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.08423","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:77dc1611487197166ca7fadaceb0678d2359d49e5d055226b0be42efedbcb275","sha256:308eaa3adc830a0fbab5c823afea495ec060b5c873a7a9dd115510ca2beead1c"],"state_sha256":"ec5cd5c2328fe9d1a8fbe523c1dcbf99141dbdc8b9621ace54c398d338019207"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1p7/JnCz7Dz3eWYtuUVg865eGnn4pKTNWV8L99DzpL21MnKg1sh3n4GU4VBKtLW+j6/GCd3maBoLwatqEvnDBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T19:12:17.823328Z","bundle_sha256":"1a099378ab3af87c78f192d2ae42ed96b2c24a0cea4bb92c8a5f726f742a5fd4"}}