{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:3LYITCNEMYMBADRA4J2LAOEOYI","short_pith_number":"pith:3LYITCNE","schema_version":"1.0","canonical_sha256":"daf08989a46618100e20e274b0388ec21de5b11ab77df647cf4300130cf62530","source":{"kind":"arxiv","id":"1504.07460","version":1},"attestation_state":"computed","paper":{"title":"Identifying Reliable Annotations for Large Scale Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Kolesnikov, Christoph H. Lampert","submitted_at":"2015-04-28T13:19:21Z","abstract_excerpt":"Challenging computer vision tasks, in particular semantic image segmentation, require large training sets of annotated images. While obtaining the actual images is often unproblematic, creating the necessary annotation is a tedious and costly process. Therefore, one often has to work with unreliable annotation sources, such as Amazon Mechanical Turk or (semi-)automatic algorithmic techniques. In this work, we present a Gaussian process (GP) based technique for simultaneously identifying which images of a training set have unreliable annotation and learning a segmentation model in which the neg"},"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":"1504.07460","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-28T13:19:21Z","cross_cats_sorted":[],"title_canon_sha256":"950d558dfbb1dacb99216d0ac8a604dbe8881b2b63d3483c748995302306051b","abstract_canon_sha256":"a2a016bba7338add711bbe152b67cd3499c8a7008afeab3074569eac69a8d3ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:17:34.655803Z","signature_b64":"0kQ45GbIuzElbz0yr44UR2gK/gt3W7KzBVh8QC5UzDIxUMvE36sUn5Dm8dZ5PxU4yukj35T0duVpN/lX5knRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"daf08989a46618100e20e274b0388ec21de5b11ab77df647cf4300130cf62530","last_reissued_at":"2026-05-18T02:17:34.655169Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:17:34.655169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identifying Reliable Annotations for Large Scale Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander Kolesnikov, Christoph H. Lampert","submitted_at":"2015-04-28T13:19:21Z","abstract_excerpt":"Challenging computer vision tasks, in particular semantic image segmentation, require large training sets of annotated images. While obtaining the actual images is often unproblematic, creating the necessary annotation is a tedious and costly process. Therefore, one often has to work with unreliable annotation sources, such as Amazon Mechanical Turk or (semi-)automatic algorithmic techniques. In this work, we present a Gaussian process (GP) based technique for simultaneously identifying which images of a training set have unreliable annotation and learning a segmentation model in which the neg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.07460","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":"1504.07460","created_at":"2026-05-18T02:17:34.655269+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.07460v1","created_at":"2026-05-18T02:17:34.655269+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.07460","created_at":"2026-05-18T02:17:34.655269+00:00"},{"alias_kind":"pith_short_12","alias_value":"3LYITCNEMYMB","created_at":"2026-05-18T12:29:02.477457+00:00"},{"alias_kind":"pith_short_16","alias_value":"3LYITCNEMYMBADRA","created_at":"2026-05-18T12:29:02.477457+00:00"},{"alias_kind":"pith_short_8","alias_value":"3LYITCNE","created_at":"2026-05-18T12:29:02.477457+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/3LYITCNEMYMBADRA4J2LAOEOYI","json":"https://pith.science/pith/3LYITCNEMYMBADRA4J2LAOEOYI.json","graph_json":"https://pith.science/api/pith-number/3LYITCNEMYMBADRA4J2LAOEOYI/graph.json","events_json":"https://pith.science/api/pith-number/3LYITCNEMYMBADRA4J2LAOEOYI/events.json","paper":"https://pith.science/paper/3LYITCNE"},"agent_actions":{"view_html":"https://pith.science/pith/3LYITCNEMYMBADRA4J2LAOEOYI","download_json":"https://pith.science/pith/3LYITCNEMYMBADRA4J2LAOEOYI.json","view_paper":"https://pith.science/paper/3LYITCNE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.07460&json=true","fetch_graph":"https://pith.science/api/pith-number/3LYITCNEMYMBADRA4J2LAOEOYI/graph.json","fetch_events":"https://pith.science/api/pith-number/3LYITCNEMYMBADRA4J2LAOEOYI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3LYITCNEMYMBADRA4J2LAOEOYI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3LYITCNEMYMBADRA4J2LAOEOYI/action/storage_attestation","attest_author":"https://pith.science/pith/3LYITCNEMYMBADRA4J2LAOEOYI/action/author_attestation","sign_citation":"https://pith.science/pith/3LYITCNEMYMBADRA4J2LAOEOYI/action/citation_signature","submit_replication":"https://pith.science/pith/3LYITCNEMYMBADRA4J2LAOEOYI/action/replication_record"}},"created_at":"2026-05-18T02:17:34.655269+00:00","updated_at":"2026-05-18T02:17:34.655269+00:00"}