{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:N2OXMGMHTSSWZASFARM5Z47MZM","short_pith_number":"pith:N2OXMGMH","schema_version":"1.0","canonical_sha256":"6e9d7619879ca56c82450459dcf3eccb093b7d6f649af912a2263514cad9ed2c","source":{"kind":"arxiv","id":"2308.06468","version":1},"attestation_state":"computed","paper":{"title":"Tiny and Efficient Model for the Edge Detection Generalization","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Angel D. Sappa, Mohammad Rouhani, Xavier Soria, Yachuan Li","submitted_at":"2023-08-12T05:23:36Z","abstract_excerpt":"Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only $58K$ parameters, le"},"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":"2308.06468","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-12T05:23:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2b021f8ed03258ab43e8f47bba0e3f6b5d271b56a7746d53ad3c42adcf96933d","abstract_canon_sha256":"a91ce1d6d5944d1841fce880c3b0ba12cb54cde08660de645d3db16fa3f4ccdc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:40:40.529587Z","signature_b64":"VJTr4kVW22KnQW2beSB4WKiU+sVobK7xNs6bpX5ZC15Oer2ZiuQ2jWTU4iGMgbmAmw3x5WZTQYJakK0bkG7QBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e9d7619879ca56c82450459dcf3eccb093b7d6f649af912a2263514cad9ed2c","last_reissued_at":"2026-07-05T06:40:40.529137Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:40:40.529137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tiny and Efficient Model for the Edge Detection Generalization","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Angel D. Sappa, Mohammad Rouhani, Xavier Soria, Yachuan Li","submitted_at":"2023-08-12T05:23:36Z","abstract_excerpt":"Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only $58K$ parameters, le"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.06468","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/2308.06468/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":"2308.06468","created_at":"2026-07-05T06:40:40.529201+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.06468v1","created_at":"2026-07-05T06:40:40.529201+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.06468","created_at":"2026-07-05T06:40:40.529201+00:00"},{"alias_kind":"pith_short_12","alias_value":"N2OXMGMHTSSW","created_at":"2026-07-05T06:40:40.529201+00:00"},{"alias_kind":"pith_short_16","alias_value":"N2OXMGMHTSSWZASF","created_at":"2026-07-05T06:40:40.529201+00:00"},{"alias_kind":"pith_short_8","alias_value":"N2OXMGMH","created_at":"2026-07-05T06:40:40.529201+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.09024","citing_title":"Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams","ref_index":71,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N2OXMGMHTSSWZASFARM5Z47MZM","json":"https://pith.science/pith/N2OXMGMHTSSWZASFARM5Z47MZM.json","graph_json":"https://pith.science/api/pith-number/N2OXMGMHTSSWZASFARM5Z47MZM/graph.json","events_json":"https://pith.science/api/pith-number/N2OXMGMHTSSWZASFARM5Z47MZM/events.json","paper":"https://pith.science/paper/N2OXMGMH"},"agent_actions":{"view_html":"https://pith.science/pith/N2OXMGMHTSSWZASFARM5Z47MZM","download_json":"https://pith.science/pith/N2OXMGMHTSSWZASFARM5Z47MZM.json","view_paper":"https://pith.science/paper/N2OXMGMH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.06468&json=true","fetch_graph":"https://pith.science/api/pith-number/N2OXMGMHTSSWZASFARM5Z47MZM/graph.json","fetch_events":"https://pith.science/api/pith-number/N2OXMGMHTSSWZASFARM5Z47MZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N2OXMGMHTSSWZASFARM5Z47MZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N2OXMGMHTSSWZASFARM5Z47MZM/action/storage_attestation","attest_author":"https://pith.science/pith/N2OXMGMHTSSWZASFARM5Z47MZM/action/author_attestation","sign_citation":"https://pith.science/pith/N2OXMGMHTSSWZASFARM5Z47MZM/action/citation_signature","submit_replication":"https://pith.science/pith/N2OXMGMHTSSWZASFARM5Z47MZM/action/replication_record"}},"created_at":"2026-07-05T06:40:40.529201+00:00","updated_at":"2026-07-05T06:40:40.529201+00:00"}