{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:TTG35AGLGYKXOBJVUDQTJ2PQTR","short_pith_number":"pith:TTG35AGL","schema_version":"1.0","canonical_sha256":"9ccdbe80cb3615770535a0e134e9f09c4448e6f211de53f72a390a0585933275","source":{"kind":"arxiv","id":"2208.10716","version":1},"attestation_state":"computed","paper":{"title":"Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunxiang Wang, Ming Yang, Weihao Yan, Yeqiang Qian","submitted_at":"2022-08-23T03:48:48Z","abstract_excerpt":"Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning methods require large amounts of labeled data for training. Manual annotation is expensive, while simulators can provide accurate annotations. However, the performance of the semantic segmentation model trained with the data of the simulator will significantly decrease when applied in the actual scene. Unsupervised domain adaptation (UDA) for semantic segmentation has recently gained increasing research attention, aiming to reduce the domain gap and improve the performance o"},"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":"2208.10716","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-23T03:48:48Z","cross_cats_sorted":[],"title_canon_sha256":"7a5d2fc530e68697877c2131db518c8800f5cff7f1040a71024f4451d70175f6","abstract_canon_sha256":"4ee8b799bbc0529be7f76be8a97705ab69ec97d417480d3c770f4f9e0364b4e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:50:38.911573Z","signature_b64":"faO5a4arHBk7aKJnxQWSfkix9RZYQPJFWnJheV2uQiRcUJPQAQ1eCAbm2tlV10kewakV1VC3uMGvV5A5feFpDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ccdbe80cb3615770535a0e134e9f09c4448e6f211de53f72a390a0585933275","last_reissued_at":"2026-07-05T04:50:38.911222Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:50:38.911222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunxiang Wang, Ming Yang, Weihao Yan, Yeqiang Qian","submitted_at":"2022-08-23T03:48:48Z","abstract_excerpt":"Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning methods require large amounts of labeled data for training. Manual annotation is expensive, while simulators can provide accurate annotations. However, the performance of the semantic segmentation model trained with the data of the simulator will significantly decrease when applied in the actual scene. Unsupervised domain adaptation (UDA) for semantic segmentation has recently gained increasing research attention, aiming to reduce the domain gap and improve the performance o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.10716","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/2208.10716/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":"2208.10716","created_at":"2026-07-05T04:50:38.911282+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.10716v1","created_at":"2026-07-05T04:50:38.911282+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.10716","created_at":"2026-07-05T04:50:38.911282+00:00"},{"alias_kind":"pith_short_12","alias_value":"TTG35AGLGYKX","created_at":"2026-07-05T04:50:38.911282+00:00"},{"alias_kind":"pith_short_16","alias_value":"TTG35AGLGYKXOBJV","created_at":"2026-07-05T04:50:38.911282+00:00"},{"alias_kind":"pith_short_8","alias_value":"TTG35AGL","created_at":"2026-07-05T04:50:38.911282+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/TTG35AGLGYKXOBJVUDQTJ2PQTR","json":"https://pith.science/pith/TTG35AGLGYKXOBJVUDQTJ2PQTR.json","graph_json":"https://pith.science/api/pith-number/TTG35AGLGYKXOBJVUDQTJ2PQTR/graph.json","events_json":"https://pith.science/api/pith-number/TTG35AGLGYKXOBJVUDQTJ2PQTR/events.json","paper":"https://pith.science/paper/TTG35AGL"},"agent_actions":{"view_html":"https://pith.science/pith/TTG35AGLGYKXOBJVUDQTJ2PQTR","download_json":"https://pith.science/pith/TTG35AGLGYKXOBJVUDQTJ2PQTR.json","view_paper":"https://pith.science/paper/TTG35AGL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.10716&json=true","fetch_graph":"https://pith.science/api/pith-number/TTG35AGLGYKXOBJVUDQTJ2PQTR/graph.json","fetch_events":"https://pith.science/api/pith-number/TTG35AGLGYKXOBJVUDQTJ2PQTR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TTG35AGLGYKXOBJVUDQTJ2PQTR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TTG35AGLGYKXOBJVUDQTJ2PQTR/action/storage_attestation","attest_author":"https://pith.science/pith/TTG35AGLGYKXOBJVUDQTJ2PQTR/action/author_attestation","sign_citation":"https://pith.science/pith/TTG35AGLGYKXOBJVUDQTJ2PQTR/action/citation_signature","submit_replication":"https://pith.science/pith/TTG35AGLGYKXOBJVUDQTJ2PQTR/action/replication_record"}},"created_at":"2026-07-05T04:50:38.911282+00:00","updated_at":"2026-07-05T04:50:38.911282+00:00"}