{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:RBSGCCETPZ66GX6I7AAEZFIO6F","short_pith_number":"pith:RBSGCCET","schema_version":"1.0","canonical_sha256":"88646108937e7de35fc8f8004c950ef171d9bacd211ae15621062129655ec3d5","source":{"kind":"arxiv","id":"2212.01322","version":2},"attestation_state":"computed","paper":{"title":"MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dengxin Dai, Haoran Wang, Luc Van Gool, Lukas Hoyer","submitted_at":"2022-12-02T17:29:32Z","abstract_excerpt":"In unsupervised domain adaptation (UDA), a model trained on source data (e.g. synthetic) is adapted to target data (e.g. real-world) without access to target annotation. Most previous UDA methods struggle with classes that have a similar visual appearance on the target domain as no ground truth is available to learn the slight appearance differences. To address this problem, we propose a Masked Image Consistency (MIC) module to enhance UDA by learning spatial context relations of the target domain as additional clues for robust visual recognition. MIC enforces the consistency between predictio"},"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":"2212.01322","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-02T17:29:32Z","cross_cats_sorted":[],"title_canon_sha256":"bf3ff83e33dffaefea128eeed73dfd3ade887501dbad2392c1e832c87ecfdf9e","abstract_canon_sha256":"aeb0f5f75cb6108f3364d0d64092a2117808e9c40afd8fb05275d87e54f707a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:54:14.232304Z","signature_b64":"vfFHLjsddnXgECXa5X+Ee6Faiyfu1chosoUhvFJIGLvzGhMwaoP+djsoTTfr/KA31Yv7snByhKRLjiN2gnmpBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88646108937e7de35fc8f8004c950ef171d9bacd211ae15621062129655ec3d5","last_reissued_at":"2026-07-05T05:54:14.231845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:54:14.231845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dengxin Dai, Haoran Wang, Luc Van Gool, Lukas Hoyer","submitted_at":"2022-12-02T17:29:32Z","abstract_excerpt":"In unsupervised domain adaptation (UDA), a model trained on source data (e.g. synthetic) is adapted to target data (e.g. real-world) without access to target annotation. Most previous UDA methods struggle with classes that have a similar visual appearance on the target domain as no ground truth is available to learn the slight appearance differences. To address this problem, we propose a Masked Image Consistency (MIC) module to enhance UDA by learning spatial context relations of the target domain as additional clues for robust visual recognition. MIC enforces the consistency between predictio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.01322","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/2212.01322/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":"2212.01322","created_at":"2026-07-05T05:54:14.231901+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.01322v2","created_at":"2026-07-05T05:54:14.231901+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.01322","created_at":"2026-07-05T05:54:14.231901+00:00"},{"alias_kind":"pith_short_12","alias_value":"RBSGCCETPZ66","created_at":"2026-07-05T05:54:14.231901+00:00"},{"alias_kind":"pith_short_16","alias_value":"RBSGCCETPZ66GX6I","created_at":"2026-07-05T05:54:14.231901+00:00"},{"alias_kind":"pith_short_8","alias_value":"RBSGCCET","created_at":"2026-07-05T05:54:14.231901+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/RBSGCCETPZ66GX6I7AAEZFIO6F","json":"https://pith.science/pith/RBSGCCETPZ66GX6I7AAEZFIO6F.json","graph_json":"https://pith.science/api/pith-number/RBSGCCETPZ66GX6I7AAEZFIO6F/graph.json","events_json":"https://pith.science/api/pith-number/RBSGCCETPZ66GX6I7AAEZFIO6F/events.json","paper":"https://pith.science/paper/RBSGCCET"},"agent_actions":{"view_html":"https://pith.science/pith/RBSGCCETPZ66GX6I7AAEZFIO6F","download_json":"https://pith.science/pith/RBSGCCETPZ66GX6I7AAEZFIO6F.json","view_paper":"https://pith.science/paper/RBSGCCET","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.01322&json=true","fetch_graph":"https://pith.science/api/pith-number/RBSGCCETPZ66GX6I7AAEZFIO6F/graph.json","fetch_events":"https://pith.science/api/pith-number/RBSGCCETPZ66GX6I7AAEZFIO6F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RBSGCCETPZ66GX6I7AAEZFIO6F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RBSGCCETPZ66GX6I7AAEZFIO6F/action/storage_attestation","attest_author":"https://pith.science/pith/RBSGCCETPZ66GX6I7AAEZFIO6F/action/author_attestation","sign_citation":"https://pith.science/pith/RBSGCCETPZ66GX6I7AAEZFIO6F/action/citation_signature","submit_replication":"https://pith.science/pith/RBSGCCETPZ66GX6I7AAEZFIO6F/action/replication_record"}},"created_at":"2026-07-05T05:54:14.231901+00:00","updated_at":"2026-07-05T05:54:14.231901+00:00"}