{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3C4SAZBXKSLBOEKIRLU34UT4NF","short_pith_number":"pith:3C4SAZBX","schema_version":"1.0","canonical_sha256":"d8b920643754961711488ae9be527c695b83c3dd66f5f17e6ee7da187608ccea","source":{"kind":"arxiv","id":"2307.02658","version":1},"attestation_state":"computed","paper":{"title":"Spherical Feature Pyramid Networks For Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Pavlos Andreadis, Thomas Walker, Varun Anand","submitted_at":"2023-07-05T21:19:13Z","abstract_excerpt":"Semantic segmentation for spherical data is a challenging problem in machine learning since conventional planar approaches require projecting the spherical image to the Euclidean plane. Representing the signal on a fundamentally different topology introduces edges and distortions which impact network performance. Recently, graph-based approaches have bypassed these challenges to attain significant improvements by representing the signal on a spherical mesh. Current approaches to spherical segmentation exclusively use variants of the UNet architecture, meaning more successful planar architectur"},"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":"2307.02658","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-05T21:19:13Z","cross_cats_sorted":[],"title_canon_sha256":"1e0bedcca357f99098df2a399a522186cb0c33f64cf913c8c9088a3f34c92984","abstract_canon_sha256":"85d30c3377314847b0a57cfd491b84aa0540fe5b8abd6011a0f0750b71796d80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:28:22.428783Z","signature_b64":"CAjLDBuKyzQkyywsBklnXNWjT3Oe2wlE5XrkQ4PyCpTfAomHhuggfhcVqUvk2eyovx1Mb0lwj9F4xkXyqBm8Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8b920643754961711488ae9be527c695b83c3dd66f5f17e6ee7da187608ccea","last_reissued_at":"2026-07-05T06:28:22.428301Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:28:22.428301Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spherical Feature Pyramid Networks For Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Pavlos Andreadis, Thomas Walker, Varun Anand","submitted_at":"2023-07-05T21:19:13Z","abstract_excerpt":"Semantic segmentation for spherical data is a challenging problem in machine learning since conventional planar approaches require projecting the spherical image to the Euclidean plane. Representing the signal on a fundamentally different topology introduces edges and distortions which impact network performance. Recently, graph-based approaches have bypassed these challenges to attain significant improvements by representing the signal on a spherical mesh. Current approaches to spherical segmentation exclusively use variants of the UNet architecture, meaning more successful planar architectur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.02658","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/2307.02658/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":"2307.02658","created_at":"2026-07-05T06:28:22.428372+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.02658v1","created_at":"2026-07-05T06:28:22.428372+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.02658","created_at":"2026-07-05T06:28:22.428372+00:00"},{"alias_kind":"pith_short_12","alias_value":"3C4SAZBXKSLB","created_at":"2026-07-05T06:28:22.428372+00:00"},{"alias_kind":"pith_short_16","alias_value":"3C4SAZBXKSLBOEKI","created_at":"2026-07-05T06:28:22.428372+00:00"},{"alias_kind":"pith_short_8","alias_value":"3C4SAZBX","created_at":"2026-07-05T06:28:22.428372+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.03765","citing_title":"Image Segmentation: Inducing graph-based learning","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3C4SAZBXKSLBOEKIRLU34UT4NF","json":"https://pith.science/pith/3C4SAZBXKSLBOEKIRLU34UT4NF.json","graph_json":"https://pith.science/api/pith-number/3C4SAZBXKSLBOEKIRLU34UT4NF/graph.json","events_json":"https://pith.science/api/pith-number/3C4SAZBXKSLBOEKIRLU34UT4NF/events.json","paper":"https://pith.science/paper/3C4SAZBX"},"agent_actions":{"view_html":"https://pith.science/pith/3C4SAZBXKSLBOEKIRLU34UT4NF","download_json":"https://pith.science/pith/3C4SAZBXKSLBOEKIRLU34UT4NF.json","view_paper":"https://pith.science/paper/3C4SAZBX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.02658&json=true","fetch_graph":"https://pith.science/api/pith-number/3C4SAZBXKSLBOEKIRLU34UT4NF/graph.json","fetch_events":"https://pith.science/api/pith-number/3C4SAZBXKSLBOEKIRLU34UT4NF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3C4SAZBXKSLBOEKIRLU34UT4NF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3C4SAZBXKSLBOEKIRLU34UT4NF/action/storage_attestation","attest_author":"https://pith.science/pith/3C4SAZBXKSLBOEKIRLU34UT4NF/action/author_attestation","sign_citation":"https://pith.science/pith/3C4SAZBXKSLBOEKIRLU34UT4NF/action/citation_signature","submit_replication":"https://pith.science/pith/3C4SAZBXKSLBOEKIRLU34UT4NF/action/replication_record"}},"created_at":"2026-07-05T06:28:22.428372+00:00","updated_at":"2026-07-05T06:28:22.428372+00:00"}