{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:E7TY5RE4TPWRYZ56FRVR3B2ZCP","short_pith_number":"pith:E7TY5RE4","schema_version":"1.0","canonical_sha256":"27e78ec49c9bed1c67be2c6b1d875913feafc35873360f7fb09330af3818308a","source":{"kind":"arxiv","id":"2401.02032","version":2},"attestation_state":"computed","paper":{"title":"DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Xu, Renjiao Yi, Yuhang Huang, Yunfan Ye, Zhiping Cai","submitted_at":"2024-01-04T02:20:54Z","abstract_excerpt":"Limited by the encoder-decoder architecture, learning-based edge detectors usually have difficulty predicting edge maps that satisfy both correctness and crispness. With the recent success of the diffusion probabilistic model (DPM), we found it is especially suitable for accurate and crisp edge detection since the denoising process is directly applied to the original image size. Therefore, we propose the first diffusion model for the task of general edge detection, which we call DiffusionEdge. To avoid expensive computational resources while retaining the final performance, we apply DPM in the"},"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":"2401.02032","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-04T02:20:54Z","cross_cats_sorted":[],"title_canon_sha256":"56df43647c5ceea6187226c72ab64977705990b9087c9a5ca7d018d4eb863123","abstract_canon_sha256":"cdda8c8c8017669421f4683a2ada496636ed6eef63e4fc7255981f7b98906d86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:31:36.600240Z","signature_b64":"qhyfSxHC3i6JPymMCOnSAZgCJeZj2wrE2bF1U6mgF/R6SCpaOaWSXiFEc9suwvoFfI8/b0t2yD6uOGCW6rCYAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27e78ec49c9bed1c67be2c6b1d875913feafc35873360f7fb09330af3818308a","last_reissued_at":"2026-07-05T07:31:36.599813Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:31:36.599813Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Xu, Renjiao Yi, Yuhang Huang, Yunfan Ye, Zhiping Cai","submitted_at":"2024-01-04T02:20:54Z","abstract_excerpt":"Limited by the encoder-decoder architecture, learning-based edge detectors usually have difficulty predicting edge maps that satisfy both correctness and crispness. With the recent success of the diffusion probabilistic model (DPM), we found it is especially suitable for accurate and crisp edge detection since the denoising process is directly applied to the original image size. Therefore, we propose the first diffusion model for the task of general edge detection, which we call DiffusionEdge. To avoid expensive computational resources while retaining the final performance, we apply DPM in the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.02032","kind":"arxiv","version":2},"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/2401.02032/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":"2401.02032","created_at":"2026-07-05T07:31:36.599863+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.02032v2","created_at":"2026-07-05T07:31:36.599863+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.02032","created_at":"2026-07-05T07:31:36.599863+00:00"},{"alias_kind":"pith_short_12","alias_value":"E7TY5RE4TPWR","created_at":"2026-07-05T07:31:36.599863+00:00"},{"alias_kind":"pith_short_16","alias_value":"E7TY5RE4TPWRYZ56","created_at":"2026-07-05T07:31:36.599863+00:00"},{"alias_kind":"pith_short_8","alias_value":"E7TY5RE4","created_at":"2026-07-05T07:31:36.599863+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":96,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E7TY5RE4TPWRYZ56FRVR3B2ZCP","json":"https://pith.science/pith/E7TY5RE4TPWRYZ56FRVR3B2ZCP.json","graph_json":"https://pith.science/api/pith-number/E7TY5RE4TPWRYZ56FRVR3B2ZCP/graph.json","events_json":"https://pith.science/api/pith-number/E7TY5RE4TPWRYZ56FRVR3B2ZCP/events.json","paper":"https://pith.science/paper/E7TY5RE4"},"agent_actions":{"view_html":"https://pith.science/pith/E7TY5RE4TPWRYZ56FRVR3B2ZCP","download_json":"https://pith.science/pith/E7TY5RE4TPWRYZ56FRVR3B2ZCP.json","view_paper":"https://pith.science/paper/E7TY5RE4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.02032&json=true","fetch_graph":"https://pith.science/api/pith-number/E7TY5RE4TPWRYZ56FRVR3B2ZCP/graph.json","fetch_events":"https://pith.science/api/pith-number/E7TY5RE4TPWRYZ56FRVR3B2ZCP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E7TY5RE4TPWRYZ56FRVR3B2ZCP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E7TY5RE4TPWRYZ56FRVR3B2ZCP/action/storage_attestation","attest_author":"https://pith.science/pith/E7TY5RE4TPWRYZ56FRVR3B2ZCP/action/author_attestation","sign_citation":"https://pith.science/pith/E7TY5RE4TPWRYZ56FRVR3B2ZCP/action/citation_signature","submit_replication":"https://pith.science/pith/E7TY5RE4TPWRYZ56FRVR3B2ZCP/action/replication_record"}},"created_at":"2026-07-05T07:31:36.599863+00:00","updated_at":"2026-07-05T07:31:36.599863+00:00"}