{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:UZYGP7QTAA3KKGVU25OZSMVVBU","short_pith_number":"pith:UZYGP7QT","schema_version":"1.0","canonical_sha256":"a67067fe130036a51ab4d75d9932b50d2f479f6f5155541abb54ccee7a774cf7","source":{"kind":"arxiv","id":"2003.08235","version":1},"attestation_state":"computed","paper":{"title":"CAFENet: Class-Agnostic Few-Shot Edge Detection Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Jaekyun Moon, Jun Seo, Young-Hyun Park","submitted_at":"2020-03-18T14:18:59Z","abstract_excerpt":"We tackle a novel few-shot learning challenge, which we call few-shot semantic edge detection, aiming to localize crisp boundaries of novel categories using only a few labeled samples. We also present a Class-Agnostic Few-shot Edge detection Network (CAFENet) based on meta-learning strategy. CAFENet employs a semantic segmentation module in small-scale to compensate for lack of semantic information in edge labels. The predicted segmentation mask is used to generate an attention map to highlight the target object region, and make the decoder module concentrate on that region. We also propose a "},"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":"2003.08235","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-18T14:18:59Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"4f77b077a0e729fc1a4e39710f8718a5f66209cd998fe0b2b79278822a6a4c08","abstract_canon_sha256":"e83ade711743bebb12d5c3192a936e4f69f76885e07c3e8bf1ccbbab904dce18"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:49:05.297236Z","signature_b64":"Wot3PwZtHKI4OPjZCCoWIIk7LA0BRHrFLFDIpf6pe8t8e580ZRs5KEn7DZ3clZbRO87yLFrG44CJwaNedI1CAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a67067fe130036a51ab4d75d9932b50d2f479f6f5155541abb54ccee7a774cf7","last_reissued_at":"2026-07-05T00:49:05.296784Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:49:05.296784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CAFENet: Class-Agnostic Few-Shot Edge Detection Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Jaekyun Moon, Jun Seo, Young-Hyun Park","submitted_at":"2020-03-18T14:18:59Z","abstract_excerpt":"We tackle a novel few-shot learning challenge, which we call few-shot semantic edge detection, aiming to localize crisp boundaries of novel categories using only a few labeled samples. We also present a Class-Agnostic Few-shot Edge detection Network (CAFENet) based on meta-learning strategy. CAFENet employs a semantic segmentation module in small-scale to compensate for lack of semantic information in edge labels. The predicted segmentation mask is used to generate an attention map to highlight the target object region, and make the decoder module concentrate on that region. We also propose a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.08235","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/2003.08235/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":"2003.08235","created_at":"2026-07-05T00:49:05.296860+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.08235v1","created_at":"2026-07-05T00:49:05.296860+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.08235","created_at":"2026-07-05T00:49:05.296860+00:00"},{"alias_kind":"pith_short_12","alias_value":"UZYGP7QTAA3K","created_at":"2026-07-05T00:49:05.296860+00:00"},{"alias_kind":"pith_short_16","alias_value":"UZYGP7QTAA3KKGVU","created_at":"2026-07-05T00:49:05.296860+00:00"},{"alias_kind":"pith_short_8","alias_value":"UZYGP7QT","created_at":"2026-07-05T00:49:05.296860+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/UZYGP7QTAA3KKGVU25OZSMVVBU","json":"https://pith.science/pith/UZYGP7QTAA3KKGVU25OZSMVVBU.json","graph_json":"https://pith.science/api/pith-number/UZYGP7QTAA3KKGVU25OZSMVVBU/graph.json","events_json":"https://pith.science/api/pith-number/UZYGP7QTAA3KKGVU25OZSMVVBU/events.json","paper":"https://pith.science/paper/UZYGP7QT"},"agent_actions":{"view_html":"https://pith.science/pith/UZYGP7QTAA3KKGVU25OZSMVVBU","download_json":"https://pith.science/pith/UZYGP7QTAA3KKGVU25OZSMVVBU.json","view_paper":"https://pith.science/paper/UZYGP7QT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.08235&json=true","fetch_graph":"https://pith.science/api/pith-number/UZYGP7QTAA3KKGVU25OZSMVVBU/graph.json","fetch_events":"https://pith.science/api/pith-number/UZYGP7QTAA3KKGVU25OZSMVVBU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UZYGP7QTAA3KKGVU25OZSMVVBU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UZYGP7QTAA3KKGVU25OZSMVVBU/action/storage_attestation","attest_author":"https://pith.science/pith/UZYGP7QTAA3KKGVU25OZSMVVBU/action/author_attestation","sign_citation":"https://pith.science/pith/UZYGP7QTAA3KKGVU25OZSMVVBU/action/citation_signature","submit_replication":"https://pith.science/pith/UZYGP7QTAA3KKGVU25OZSMVVBU/action/replication_record"}},"created_at":"2026-07-05T00:49:05.296860+00:00","updated_at":"2026-07-05T00:49:05.296860+00:00"}