{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7Z5LPVS2CCXRLI3Z66MGCN6TZW","short_pith_number":"pith:7Z5LPVS2","schema_version":"1.0","canonical_sha256":"fe7ab7d65a10af15a379f7986137d3cd9afcae30d33edaea76cdb09bfcb1df1a","source":{"kind":"arxiv","id":"1908.10638","version":1},"attestation_state":"computed","paper":{"title":"Self-supervised blur detection from synthetically blurred scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrian Galdran, Aitor Alvarez-Gila, Estibaliz Garrote, Joost Van De Weijer","submitted_at":"2019-08-28T10:58:55Z","abstract_excerpt":"Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labour intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to "},"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":"1908.10638","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-28T10:58:55Z","cross_cats_sorted":[],"title_canon_sha256":"d12d933e97985a636f127079db602ba0b8ac5a3e0e8d06e907bf66253e3a50d7","abstract_canon_sha256":"b8d4ff06fd17884eefe433bc9e5d2fe94a0a5ba236d208fb5c87bad7796ce759"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:00:19.348063Z","signature_b64":"AfPMOiIHBYAh8Q28vvv4XhWP4LNt1vCNFv+UlZOAy//SChmCTU+0VYj4/MBwExr17Rt24oHnDTJDXdQeCdYTDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe7ab7d65a10af15a379f7986137d3cd9afcae30d33edaea76cdb09bfcb1df1a","last_reissued_at":"2026-07-05T00:00:19.347640Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:00:19.347640Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-supervised blur detection from synthetically blurred scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrian Galdran, Aitor Alvarez-Gila, Estibaliz Garrote, Joost Van De Weijer","submitted_at":"2019-08-28T10:58:55Z","abstract_excerpt":"Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labour intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.10638","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/1908.10638/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1908.10638","created_at":"2026-07-05T00:00:19.347707+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.10638v1","created_at":"2026-07-05T00:00:19.347707+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.10638","created_at":"2026-07-05T00:00:19.347707+00:00"},{"alias_kind":"pith_short_12","alias_value":"7Z5LPVS2CCXR","created_at":"2026-07-05T00:00:19.347707+00:00"},{"alias_kind":"pith_short_16","alias_value":"7Z5LPVS2CCXRLI3Z","created_at":"2026-07-05T00:00:19.347707+00:00"},{"alias_kind":"pith_short_8","alias_value":"7Z5LPVS2","created_at":"2026-07-05T00:00:19.347707+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/7Z5LPVS2CCXRLI3Z66MGCN6TZW","json":"https://pith.science/pith/7Z5LPVS2CCXRLI3Z66MGCN6TZW.json","graph_json":"https://pith.science/api/pith-number/7Z5LPVS2CCXRLI3Z66MGCN6TZW/graph.json","events_json":"https://pith.science/api/pith-number/7Z5LPVS2CCXRLI3Z66MGCN6TZW/events.json","paper":"https://pith.science/paper/7Z5LPVS2"},"agent_actions":{"view_html":"https://pith.science/pith/7Z5LPVS2CCXRLI3Z66MGCN6TZW","download_json":"https://pith.science/pith/7Z5LPVS2CCXRLI3Z66MGCN6TZW.json","view_paper":"https://pith.science/paper/7Z5LPVS2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.10638&json=true","fetch_graph":"https://pith.science/api/pith-number/7Z5LPVS2CCXRLI3Z66MGCN6TZW/graph.json","fetch_events":"https://pith.science/api/pith-number/7Z5LPVS2CCXRLI3Z66MGCN6TZW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7Z5LPVS2CCXRLI3Z66MGCN6TZW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7Z5LPVS2CCXRLI3Z66MGCN6TZW/action/storage_attestation","attest_author":"https://pith.science/pith/7Z5LPVS2CCXRLI3Z66MGCN6TZW/action/author_attestation","sign_citation":"https://pith.science/pith/7Z5LPVS2CCXRLI3Z66MGCN6TZW/action/citation_signature","submit_replication":"https://pith.science/pith/7Z5LPVS2CCXRLI3Z66MGCN6TZW/action/replication_record"}},"created_at":"2026-07-05T00:00:19.347707+00:00","updated_at":"2026-07-05T00:00:19.347707+00:00"}