{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:GVG62JL45FBHR6IJ6TTOFRNGWC","short_pith_number":"pith:GVG62JL4","schema_version":"1.0","canonical_sha256":"354ded257ce94278f909f4e6e2c5a6b09b047ed07c78aa83d8f2a9b05f21f02f","source":{"kind":"arxiv","id":"2103.16694","version":2},"attestation_state":"computed","paper":{"title":"Geometric Unsupervised Domain Adaptation for Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrien Gaidon, Jie Li, Rares Ambrus, Vitor Guizilini","submitted_at":"2021-03-30T21:33:00Z","abstract_excerpt":"Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation. However, they introduce a domain gap that severely hurts real-world performance. We propose to use self-supervised monocular depth estimation as a proxy task to bridge this gap and improve sim-to-real unsupervised domain adaptation (UDA). Our Geometric Unsupervised Domain Adaptation method (GUDA) learns a domain-invariant representation via a multi-task objective combining synthetic semantic supervision with real-world geometric constraints on"},"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":"2103.16694","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-30T21:33:00Z","cross_cats_sorted":[],"title_canon_sha256":"c6fde76648b7d5d8c979d79f3662fc14e9fdf3ee4eb3488689a50e525a8a1507","abstract_canon_sha256":"b9d3e3e10cda2843b1cdcbdcf7ce30894287a6c643b9a1dd03f79c4ad2ded66d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:06:46.454851Z","signature_b64":"nynuGVeBkk6BRhQge3ynDKxOqGxM2MmMjVBCjR+akrtNFq6orEmW6XHhWGeDj44pSW0BMqrkKKpoOhwJpUqkBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"354ded257ce94278f909f4e6e2c5a6b09b047ed07c78aa83d8f2a9b05f21f02f","last_reissued_at":"2026-07-05T03:06:46.454428Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:06:46.454428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Geometric Unsupervised Domain Adaptation for Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrien Gaidon, Jie Li, Rares Ambrus, Vitor Guizilini","submitted_at":"2021-03-30T21:33:00Z","abstract_excerpt":"Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation. However, they introduce a domain gap that severely hurts real-world performance. We propose to use self-supervised monocular depth estimation as a proxy task to bridge this gap and improve sim-to-real unsupervised domain adaptation (UDA). Our Geometric Unsupervised Domain Adaptation method (GUDA) learns a domain-invariant representation via a multi-task objective combining synthetic semantic supervision with real-world geometric constraints on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.16694","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/2103.16694/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":"2103.16694","created_at":"2026-07-05T03:06:46.454489+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.16694v2","created_at":"2026-07-05T03:06:46.454489+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.16694","created_at":"2026-07-05T03:06:46.454489+00:00"},{"alias_kind":"pith_short_12","alias_value":"GVG62JL45FBH","created_at":"2026-07-05T03:06:46.454489+00:00"},{"alias_kind":"pith_short_16","alias_value":"GVG62JL45FBHR6IJ","created_at":"2026-07-05T03:06:46.454489+00:00"},{"alias_kind":"pith_short_8","alias_value":"GVG62JL4","created_at":"2026-07-05T03:06:46.454489+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/GVG62JL45FBHR6IJ6TTOFRNGWC","json":"https://pith.science/pith/GVG62JL45FBHR6IJ6TTOFRNGWC.json","graph_json":"https://pith.science/api/pith-number/GVG62JL45FBHR6IJ6TTOFRNGWC/graph.json","events_json":"https://pith.science/api/pith-number/GVG62JL45FBHR6IJ6TTOFRNGWC/events.json","paper":"https://pith.science/paper/GVG62JL4"},"agent_actions":{"view_html":"https://pith.science/pith/GVG62JL45FBHR6IJ6TTOFRNGWC","download_json":"https://pith.science/pith/GVG62JL45FBHR6IJ6TTOFRNGWC.json","view_paper":"https://pith.science/paper/GVG62JL4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.16694&json=true","fetch_graph":"https://pith.science/api/pith-number/GVG62JL45FBHR6IJ6TTOFRNGWC/graph.json","fetch_events":"https://pith.science/api/pith-number/GVG62JL45FBHR6IJ6TTOFRNGWC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GVG62JL45FBHR6IJ6TTOFRNGWC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GVG62JL45FBHR6IJ6TTOFRNGWC/action/storage_attestation","attest_author":"https://pith.science/pith/GVG62JL45FBHR6IJ6TTOFRNGWC/action/author_attestation","sign_citation":"https://pith.science/pith/GVG62JL45FBHR6IJ6TTOFRNGWC/action/citation_signature","submit_replication":"https://pith.science/pith/GVG62JL45FBHR6IJ6TTOFRNGWC/action/replication_record"}},"created_at":"2026-07-05T03:06:46.454489+00:00","updated_at":"2026-07-05T03:06:46.454489+00:00"}