{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5UA6RE5C7DJPLUE66RWUNL34YE","short_pith_number":"pith:5UA6RE5C","schema_version":"1.0","canonical_sha256":"ed01e893a2f8d2f5d09ef46d46af7cc10f365228e578ba1e76ede0e854edfd51","source":{"kind":"arxiv","id":"2501.03765","version":2},"attestation_state":"computed","paper":{"title":"Image Segmentation: Inducing graph-based learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Aryan Singh, Ciar\\'an Eising, Patrick Denny, Pepijn Van de Ven","submitted_at":"2025-01-07T13:09:44Z","abstract_excerpt":"This study explores the potential of graph neural networks (GNNs) to enhance semantic segmentation across diverse image modalities. We evaluate the effectiveness of a novel GNN-based U-Net architecture on three distinct datasets: PascalVOC, a standard benchmark for natural image segmentation, WoodScape, a challenging dataset of fisheye images commonly used in autonomous driving, introducing significant geometric distortions; and ISIC2016, a dataset of dermoscopic images for skin lesion segmentation. We compare our proposed UNet-GNN model against established convolutional neural networks (CNNs)"},"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":"2501.03765","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-07T13:09:44Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"0ba76372126c105489b0bfaae910e28e1f01a8f934c566b6cd66025eb882718e","abstract_canon_sha256":"1ff6beaceb0a7ffb6099f373943dbc1eb34ae282ceb6e931f5d2011c559ba38a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:57.521194Z","signature_b64":"wc4X1VidYbKc5/NWy2q5cQpSx8kZmjMFLEqR+VDWp0Djw3sbENcjLbJRNiPS6EcpMSLwF0IiDlyJKCWmkGuTCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ed01e893a2f8d2f5d09ef46d46af7cc10f365228e578ba1e76ede0e854edfd51","last_reissued_at":"2026-07-05T10:02:57.520669Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:57.520669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Image Segmentation: Inducing graph-based learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Aryan Singh, Ciar\\'an Eising, Patrick Denny, Pepijn Van de Ven","submitted_at":"2025-01-07T13:09:44Z","abstract_excerpt":"This study explores the potential of graph neural networks (GNNs) to enhance semantic segmentation across diverse image modalities. We evaluate the effectiveness of a novel GNN-based U-Net architecture on three distinct datasets: PascalVOC, a standard benchmark for natural image segmentation, WoodScape, a challenging dataset of fisheye images commonly used in autonomous driving, introducing significant geometric distortions; and ISIC2016, a dataset of dermoscopic images for skin lesion segmentation. We compare our proposed UNet-GNN model against established convolutional neural networks (CNNs)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.03765","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/2501.03765/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":"2501.03765","created_at":"2026-07-05T10:02:57.520741+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.03765v2","created_at":"2026-07-05T10:02:57.520741+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.03765","created_at":"2026-07-05T10:02:57.520741+00:00"},{"alias_kind":"pith_short_12","alias_value":"5UA6RE5C7DJP","created_at":"2026-07-05T10:02:57.520741+00:00"},{"alias_kind":"pith_short_16","alias_value":"5UA6RE5C7DJPLUE6","created_at":"2026-07-05T10:02:57.520741+00:00"},{"alias_kind":"pith_short_8","alias_value":"5UA6RE5C","created_at":"2026-07-05T10:02:57.520741+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.24234","citing_title":"Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5UA6RE5C7DJPLUE66RWUNL34YE","json":"https://pith.science/pith/5UA6RE5C7DJPLUE66RWUNL34YE.json","graph_json":"https://pith.science/api/pith-number/5UA6RE5C7DJPLUE66RWUNL34YE/graph.json","events_json":"https://pith.science/api/pith-number/5UA6RE5C7DJPLUE66RWUNL34YE/events.json","paper":"https://pith.science/paper/5UA6RE5C"},"agent_actions":{"view_html":"https://pith.science/pith/5UA6RE5C7DJPLUE66RWUNL34YE","download_json":"https://pith.science/pith/5UA6RE5C7DJPLUE66RWUNL34YE.json","view_paper":"https://pith.science/paper/5UA6RE5C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.03765&json=true","fetch_graph":"https://pith.science/api/pith-number/5UA6RE5C7DJPLUE66RWUNL34YE/graph.json","fetch_events":"https://pith.science/api/pith-number/5UA6RE5C7DJPLUE66RWUNL34YE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5UA6RE5C7DJPLUE66RWUNL34YE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5UA6RE5C7DJPLUE66RWUNL34YE/action/storage_attestation","attest_author":"https://pith.science/pith/5UA6RE5C7DJPLUE66RWUNL34YE/action/author_attestation","sign_citation":"https://pith.science/pith/5UA6RE5C7DJPLUE66RWUNL34YE/action/citation_signature","submit_replication":"https://pith.science/pith/5UA6RE5C7DJPLUE66RWUNL34YE/action/replication_record"}},"created_at":"2026-07-05T10:02:57.520741+00:00","updated_at":"2026-07-05T10:02:57.520741+00:00"}