{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2IRD5QWEVLOU4TJOYX453GYDLC","short_pith_number":"pith:2IRD5QWE","schema_version":"1.0","canonical_sha256":"d2223ec2c4aadd4e4d2ec5f9dd9b035886e57396e74e080747ff019c62d9b73e","source":{"kind":"arxiv","id":"1807.11847","version":1},"attestation_state":"computed","paper":{"title":"Fast Sketch Segmentation and Labeling with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Chiew-Lan Tai, Hongbo Fu, Lei Li","submitted_at":"2018-07-31T14:56:02Z","abstract_excerpt":"We present a simple and efficient method based on deep learning to automatically decompose sketched objects into semantically valid parts. We train a deep neural network to transfer existing segmentations and labelings from 3D models to freehand sketches without requiring numerous well-annotated sketches as training data. The network takes the binary image of a sketched object as input and produces a corresponding segmentation map with per-pixel labelings as output. A subsequent post-process procedure with multi-label graph cuts further refines the segmentation and labeling result. We validate"},"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":"1807.11847","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2018-07-31T14:56:02Z","cross_cats_sorted":[],"title_canon_sha256":"68564ee9017e36779e194bfa5c151c7a34a2f069ed57258cde436190d9c66746","abstract_canon_sha256":"a3791066ab3fd6926a9c528ae7839a5f22b0a615e93698846d28a844166f3e89"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:20.927286Z","signature_b64":"hWqhRPbbMA5BxNHJYHjaiieDjooYOWV6ca9aBYZOJZ93ip7x6ida6pxIreqAr0jqFFKcpIdGfAPCcsHQuEjSAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2223ec2c4aadd4e4d2ec5f9dd9b035886e57396e74e080747ff019c62d9b73e","last_reissued_at":"2026-05-18T00:09:20.926793Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:20.926793Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Sketch Segmentation and Labeling with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Chiew-Lan Tai, Hongbo Fu, Lei Li","submitted_at":"2018-07-31T14:56:02Z","abstract_excerpt":"We present a simple and efficient method based on deep learning to automatically decompose sketched objects into semantically valid parts. We train a deep neural network to transfer existing segmentations and labelings from 3D models to freehand sketches without requiring numerous well-annotated sketches as training data. The network takes the binary image of a sketched object as input and produces a corresponding segmentation map with per-pixel labelings as output. A subsequent post-process procedure with multi-label graph cuts further refines the segmentation and labeling result. We validate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.11847","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":"1807.11847","created_at":"2026-05-18T00:09:20.926868+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.11847v1","created_at":"2026-05-18T00:09:20.926868+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.11847","created_at":"2026-05-18T00:09:20.926868+00:00"},{"alias_kind":"pith_short_12","alias_value":"2IRD5QWEVLOU","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"2IRD5QWEVLOU4TJO","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"2IRD5QWE","created_at":"2026-05-18T12:32:02.567920+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/2IRD5QWEVLOU4TJOYX453GYDLC","json":"https://pith.science/pith/2IRD5QWEVLOU4TJOYX453GYDLC.json","graph_json":"https://pith.science/api/pith-number/2IRD5QWEVLOU4TJOYX453GYDLC/graph.json","events_json":"https://pith.science/api/pith-number/2IRD5QWEVLOU4TJOYX453GYDLC/events.json","paper":"https://pith.science/paper/2IRD5QWE"},"agent_actions":{"view_html":"https://pith.science/pith/2IRD5QWEVLOU4TJOYX453GYDLC","download_json":"https://pith.science/pith/2IRD5QWEVLOU4TJOYX453GYDLC.json","view_paper":"https://pith.science/paper/2IRD5QWE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.11847&json=true","fetch_graph":"https://pith.science/api/pith-number/2IRD5QWEVLOU4TJOYX453GYDLC/graph.json","fetch_events":"https://pith.science/api/pith-number/2IRD5QWEVLOU4TJOYX453GYDLC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2IRD5QWEVLOU4TJOYX453GYDLC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2IRD5QWEVLOU4TJOYX453GYDLC/action/storage_attestation","attest_author":"https://pith.science/pith/2IRD5QWEVLOU4TJOYX453GYDLC/action/author_attestation","sign_citation":"https://pith.science/pith/2IRD5QWEVLOU4TJOYX453GYDLC/action/citation_signature","submit_replication":"https://pith.science/pith/2IRD5QWEVLOU4TJOYX453GYDLC/action/replication_record"}},"created_at":"2026-05-18T00:09:20.926868+00:00","updated_at":"2026-05-18T00:09:20.926868+00:00"}