{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YXNOAJYTBOA2BGIVCS7ZS545QI","short_pith_number":"pith:YXNOAJYT","schema_version":"1.0","canonical_sha256":"c5dae027130b81a0991514bf99779d821720fa29855867da5c1a4646b232e186","source":{"kind":"arxiv","id":"1905.01220","version":1},"attestation_state":"computed","paper":{"title":"Seamless Scene Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aleksander Colovic, Lorenzo Porzi, Peter Kontschieder, Samuel Rota Bul\\`o","submitted_at":"2019-05-03T15:21:25Z","abstract_excerpt":"In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results. Our goal is to predict consistent semantic segmentation and detection results by means of a panoptic output format, going beyond the simple combination of independently trained segmentation and detection models. The proposed architecture takes advantage of a novel segmentation head that seamlessly integrates multi-scale features generated by a Feature Pyramid Network with contextual information conveyed by a light-weight DeepLab-like module. As additional con"},"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":"1905.01220","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-03T15:21:25Z","cross_cats_sorted":[],"title_canon_sha256":"72e70c42c3225b6e2483bcc28a1f5a19778b0c72b9ccaf38bfaeb3907da70f56","abstract_canon_sha256":"28229f2bcdb93d10ff34d8b5d0f0ffa64205b2d943caca98be8e0d2b45427629"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:06.472892Z","signature_b64":"UNlAsLx9cgli1EZ6gw19A+3fajiMHXuHj4PHrTsKRZJKCRTC4muQ+rwUO+Hj81E1fU1xOHfKG5UEuOgwd6YYDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5dae027130b81a0991514bf99779d821720fa29855867da5c1a4646b232e186","last_reissued_at":"2026-05-17T23:47:06.472359Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:06.472359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Seamless Scene Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aleksander Colovic, Lorenzo Porzi, Peter Kontschieder, Samuel Rota Bul\\`o","submitted_at":"2019-05-03T15:21:25Z","abstract_excerpt":"In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results. Our goal is to predict consistent semantic segmentation and detection results by means of a panoptic output format, going beyond the simple combination of independently trained segmentation and detection models. The proposed architecture takes advantage of a novel segmentation head that seamlessly integrates multi-scale features generated by a Feature Pyramid Network with contextual information conveyed by a light-weight DeepLab-like module. As additional con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01220","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":"1905.01220","created_at":"2026-05-17T23:47:06.472441+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.01220v1","created_at":"2026-05-17T23:47:06.472441+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01220","created_at":"2026-05-17T23:47:06.472441+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXNOAJYTBOA2","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXNOAJYTBOA2BGIV","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXNOAJYT","created_at":"2026-05-18T12:33:33.725879+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/YXNOAJYTBOA2BGIVCS7ZS545QI","json":"https://pith.science/pith/YXNOAJYTBOA2BGIVCS7ZS545QI.json","graph_json":"https://pith.science/api/pith-number/YXNOAJYTBOA2BGIVCS7ZS545QI/graph.json","events_json":"https://pith.science/api/pith-number/YXNOAJYTBOA2BGIVCS7ZS545QI/events.json","paper":"https://pith.science/paper/YXNOAJYT"},"agent_actions":{"view_html":"https://pith.science/pith/YXNOAJYTBOA2BGIVCS7ZS545QI","download_json":"https://pith.science/pith/YXNOAJYTBOA2BGIVCS7ZS545QI.json","view_paper":"https://pith.science/paper/YXNOAJYT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.01220&json=true","fetch_graph":"https://pith.science/api/pith-number/YXNOAJYTBOA2BGIVCS7ZS545QI/graph.json","fetch_events":"https://pith.science/api/pith-number/YXNOAJYTBOA2BGIVCS7ZS545QI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXNOAJYTBOA2BGIVCS7ZS545QI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXNOAJYTBOA2BGIVCS7ZS545QI/action/storage_attestation","attest_author":"https://pith.science/pith/YXNOAJYTBOA2BGIVCS7ZS545QI/action/author_attestation","sign_citation":"https://pith.science/pith/YXNOAJYTBOA2BGIVCS7ZS545QI/action/citation_signature","submit_replication":"https://pith.science/pith/YXNOAJYTBOA2BGIVCS7ZS545QI/action/replication_record"}},"created_at":"2026-05-17T23:47:06.472441+00:00","updated_at":"2026-05-17T23:47:06.472441+00:00"}