{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:36ZIMLAV6AJC3CMCCGMIB5GKHL","short_pith_number":"pith:36ZIMLAV","schema_version":"1.0","canonical_sha256":"dfb2862c15f0122d8982119880f4ca3adefa0aa71034137c1fd220cc49274477","source":{"kind":"arxiv","id":"1807.07473","version":1},"attestation_state":"computed","paper":{"title":"Three for one and one for three: Flow, Segmentation, and Surface Normals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anil S. Baslamisli, Hoang-An Le, Theo Gevers, Thomas Mensink","submitted_at":"2018-07-19T14:54:21Z","abstract_excerpt":"Optical flow, semantic segmentation, and surface normals represent different information modalities, yet together they bring better cues for scene understanding problems. In this paper, we study the influence between the three modalities: how one impacts on the others and their efficiency in combination. We employ a modular approach using a convolutional refinement network which is trained supervised but isolated from RGB images to enforce joint modality features. To assist the training process, we create a large-scale synthetic outdoor dataset that supports dense annotation of semantic segmen"},"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":"1807.07473","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-19T14:54:21Z","cross_cats_sorted":[],"title_canon_sha256":"21afad71439154b1640773422b3ef2e7f6e8bc4e57f421daf207fa86bb748f16","abstract_canon_sha256":"0770c9c9fc63e572cfeb0d43cf081ca2626e603e92a55a7596b2ebf6fd5a32fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:20.052993Z","signature_b64":"Y6m2qxxCL+sYbQmxEuJv24WxujXlW8CYNlGVeD7v8f92DWYbrrfgyOz8I9HvsqW29fk8PvFxQL6GjpLj+ZG0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dfb2862c15f0122d8982119880f4ca3adefa0aa71034137c1fd220cc49274477","last_reissued_at":"2026-05-18T00:10:20.052501Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:20.052501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Three for one and one for three: Flow, Segmentation, and Surface Normals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anil S. Baslamisli, Hoang-An Le, Theo Gevers, Thomas Mensink","submitted_at":"2018-07-19T14:54:21Z","abstract_excerpt":"Optical flow, semantic segmentation, and surface normals represent different information modalities, yet together they bring better cues for scene understanding problems. In this paper, we study the influence between the three modalities: how one impacts on the others and their efficiency in combination. We employ a modular approach using a convolutional refinement network which is trained supervised but isolated from RGB images to enforce joint modality features. To assist the training process, we create a large-scale synthetic outdoor dataset that supports dense annotation of semantic segmen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.07473","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":"1807.07473","created_at":"2026-05-18T00:10:20.052572+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.07473v1","created_at":"2026-05-18T00:10:20.052572+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.07473","created_at":"2026-05-18T00:10:20.052572+00:00"},{"alias_kind":"pith_short_12","alias_value":"36ZIMLAV6AJC","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"36ZIMLAV6AJC3CMC","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"36ZIMLAV","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/36ZIMLAV6AJC3CMCCGMIB5GKHL","json":"https://pith.science/pith/36ZIMLAV6AJC3CMCCGMIB5GKHL.json","graph_json":"https://pith.science/api/pith-number/36ZIMLAV6AJC3CMCCGMIB5GKHL/graph.json","events_json":"https://pith.science/api/pith-number/36ZIMLAV6AJC3CMCCGMIB5GKHL/events.json","paper":"https://pith.science/paper/36ZIMLAV"},"agent_actions":{"view_html":"https://pith.science/pith/36ZIMLAV6AJC3CMCCGMIB5GKHL","download_json":"https://pith.science/pith/36ZIMLAV6AJC3CMCCGMIB5GKHL.json","view_paper":"https://pith.science/paper/36ZIMLAV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.07473&json=true","fetch_graph":"https://pith.science/api/pith-number/36ZIMLAV6AJC3CMCCGMIB5GKHL/graph.json","fetch_events":"https://pith.science/api/pith-number/36ZIMLAV6AJC3CMCCGMIB5GKHL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/36ZIMLAV6AJC3CMCCGMIB5GKHL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/36ZIMLAV6AJC3CMCCGMIB5GKHL/action/storage_attestation","attest_author":"https://pith.science/pith/36ZIMLAV6AJC3CMCCGMIB5GKHL/action/author_attestation","sign_citation":"https://pith.science/pith/36ZIMLAV6AJC3CMCCGMIB5GKHL/action/citation_signature","submit_replication":"https://pith.science/pith/36ZIMLAV6AJC3CMCCGMIB5GKHL/action/replication_record"}},"created_at":"2026-05-18T00:10:20.052572+00:00","updated_at":"2026-05-18T00:10:20.052572+00:00"}