{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QQ2R2EQ4UAMGG7QOTDMJZWGSFB","short_pith_number":"pith:QQ2R2EQ4","schema_version":"1.0","canonical_sha256":"84351d121ca018637e0e98d89cd8d22864e9024d21d3f2e40998f3bbbcecaa79","source":{"kind":"arxiv","id":"2501.09040","version":1},"attestation_state":"computed","paper":{"title":"Pseudolabel guided pixels contrast for domain adaptive semantic segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cailu Wan, Jianzi Xiang, Zhu Cao","submitted_at":"2025-01-15T03:25:25Z","abstract_excerpt":"Semantic segmentation is essential for comprehending images, but the process necessitates a substantial amount of detailed annotations at the pixel level. Acquiring such annotations can be costly in the real-world. Unsupervised domain adaptation (UDA) for semantic segmentation is a technique that uses virtual data with labels to train a model and adapts it to real data without labels. Some recent works use contrastive learning, which is a powerful method for self-supervised learning, to help with this technique. However, these works do not take into account the diversity of features within eac"},"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":"2501.09040","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-15T03:25:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"48e845aa2bea02c86ec7bd4b86b6177dfc3c9e86365298fbc922632c7e5b7837","abstract_canon_sha256":"47218186e2c20782fa048a0c235a66a2e0414a039c9cfd647a8975560a031089"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:01:40.787802Z","signature_b64":"j/tv3MbUjCyDnl/6hquYS9VaaRxfI1rmEQohnRweBT+hXAw52OBVCPvupCGgXBIl7i2u7AFun53+5LTBT2GzDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84351d121ca018637e0e98d89cd8d22864e9024d21d3f2e40998f3bbbcecaa79","last_reissued_at":"2026-07-05T10:01:40.787345Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:01:40.787345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pseudolabel guided pixels contrast for domain adaptive semantic segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cailu Wan, Jianzi Xiang, Zhu Cao","submitted_at":"2025-01-15T03:25:25Z","abstract_excerpt":"Semantic segmentation is essential for comprehending images, but the process necessitates a substantial amount of detailed annotations at the pixel level. Acquiring such annotations can be costly in the real-world. Unsupervised domain adaptation (UDA) for semantic segmentation is a technique that uses virtual data with labels to train a model and adapts it to real data without labels. Some recent works use contrastive learning, which is a powerful method for self-supervised learning, to help with this technique. However, these works do not take into account the diversity of features within eac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09040","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/2501.09040/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":"2501.09040","created_at":"2026-07-05T10:01:40.787405+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.09040v1","created_at":"2026-07-05T10:01:40.787405+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09040","created_at":"2026-07-05T10:01:40.787405+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQ2R2EQ4UAMG","created_at":"2026-07-05T10:01:40.787405+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQ2R2EQ4UAMGG7QO","created_at":"2026-07-05T10:01:40.787405+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQ2R2EQ4","created_at":"2026-07-05T10:01:40.787405+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/QQ2R2EQ4UAMGG7QOTDMJZWGSFB","json":"https://pith.science/pith/QQ2R2EQ4UAMGG7QOTDMJZWGSFB.json","graph_json":"https://pith.science/api/pith-number/QQ2R2EQ4UAMGG7QOTDMJZWGSFB/graph.json","events_json":"https://pith.science/api/pith-number/QQ2R2EQ4UAMGG7QOTDMJZWGSFB/events.json","paper":"https://pith.science/paper/QQ2R2EQ4"},"agent_actions":{"view_html":"https://pith.science/pith/QQ2R2EQ4UAMGG7QOTDMJZWGSFB","download_json":"https://pith.science/pith/QQ2R2EQ4UAMGG7QOTDMJZWGSFB.json","view_paper":"https://pith.science/paper/QQ2R2EQ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.09040&json=true","fetch_graph":"https://pith.science/api/pith-number/QQ2R2EQ4UAMGG7QOTDMJZWGSFB/graph.json","fetch_events":"https://pith.science/api/pith-number/QQ2R2EQ4UAMGG7QOTDMJZWGSFB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQ2R2EQ4UAMGG7QOTDMJZWGSFB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQ2R2EQ4UAMGG7QOTDMJZWGSFB/action/storage_attestation","attest_author":"https://pith.science/pith/QQ2R2EQ4UAMGG7QOTDMJZWGSFB/action/author_attestation","sign_citation":"https://pith.science/pith/QQ2R2EQ4UAMGG7QOTDMJZWGSFB/action/citation_signature","submit_replication":"https://pith.science/pith/QQ2R2EQ4UAMGG7QOTDMJZWGSFB/action/replication_record"}},"created_at":"2026-07-05T10:01:40.787405+00:00","updated_at":"2026-07-05T10:01:40.787405+00:00"}