{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UZ4PHW3ZTYAXYD263FTDSLMXBY","short_pith_number":"pith:UZ4PHW3Z","schema_version":"1.0","canonical_sha256":"a678f3db799e017c0f5ed966392d970e37b3c0dcf4a36ddb30e680d5a9375d35","source":{"kind":"arxiv","id":"2401.00420","version":2},"attestation_state":"computed","paper":{"title":"SynCDR : Training Cross Domain Retrieval Models with Synthetic Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Carlos D. Castillo, Hongcheng Wang, Kate Saenko, Samarth Mishra, Venkatesh Saligrama","submitted_at":"2023-12-31T08:06:53Z","abstract_excerpt":"In cross-domain retrieval, a model is required to identify images from the same semantic category across two visual domains. For instance, given a sketch of an object, a model needs to retrieve a real image of it from an online store's catalog. A standard approach for such a problem is learning a feature space of images where Euclidean distances reflect similarity. Even without human annotations, which may be expensive to acquire, prior methods function reasonably well using unlabeled images for training. Our problem constraint takes this further to scenarios where the two domains do not neces"},"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":"2401.00420","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-31T08:06:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"186b231d81a99b3d94f4aaf2644d851249793c292dcf8e2a0843d86bddc1117f","abstract_canon_sha256":"c60a47849dd8cf2cbb9629031dd8b326fbf52ada90a67b98135f448e48ee9640"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:48.219551Z","signature_b64":"hNQVukZrHATVsoOXUZHe99txRZXrP23bDi49tchpWOIeLK5k4Otv0YF2Ip30MFuFFCMnclnDKt3t8bUuBvOCBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a678f3db799e017c0f5ed966392d970e37b3c0dcf4a36ddb30e680d5a9375d35","last_reissued_at":"2026-07-05T07:57:48.219098Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:48.219098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SynCDR : Training Cross Domain Retrieval Models with Synthetic Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Carlos D. Castillo, Hongcheng Wang, Kate Saenko, Samarth Mishra, Venkatesh Saligrama","submitted_at":"2023-12-31T08:06:53Z","abstract_excerpt":"In cross-domain retrieval, a model is required to identify images from the same semantic category across two visual domains. For instance, given a sketch of an object, a model needs to retrieve a real image of it from an online store's catalog. A standard approach for such a problem is learning a feature space of images where Euclidean distances reflect similarity. Even without human annotations, which may be expensive to acquire, prior methods function reasonably well using unlabeled images for training. Our problem constraint takes this further to scenarios where the two domains do not neces"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.00420","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/2401.00420/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":"2401.00420","created_at":"2026-07-05T07:57:48.219156+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.00420v2","created_at":"2026-07-05T07:57:48.219156+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.00420","created_at":"2026-07-05T07:57:48.219156+00:00"},{"alias_kind":"pith_short_12","alias_value":"UZ4PHW3ZTYAX","created_at":"2026-07-05T07:57:48.219156+00:00"},{"alias_kind":"pith_short_16","alias_value":"UZ4PHW3ZTYAXYD26","created_at":"2026-07-05T07:57:48.219156+00:00"},{"alias_kind":"pith_short_8","alias_value":"UZ4PHW3Z","created_at":"2026-07-05T07:57:48.219156+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/UZ4PHW3ZTYAXYD263FTDSLMXBY","json":"https://pith.science/pith/UZ4PHW3ZTYAXYD263FTDSLMXBY.json","graph_json":"https://pith.science/api/pith-number/UZ4PHW3ZTYAXYD263FTDSLMXBY/graph.json","events_json":"https://pith.science/api/pith-number/UZ4PHW3ZTYAXYD263FTDSLMXBY/events.json","paper":"https://pith.science/paper/UZ4PHW3Z"},"agent_actions":{"view_html":"https://pith.science/pith/UZ4PHW3ZTYAXYD263FTDSLMXBY","download_json":"https://pith.science/pith/UZ4PHW3ZTYAXYD263FTDSLMXBY.json","view_paper":"https://pith.science/paper/UZ4PHW3Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.00420&json=true","fetch_graph":"https://pith.science/api/pith-number/UZ4PHW3ZTYAXYD263FTDSLMXBY/graph.json","fetch_events":"https://pith.science/api/pith-number/UZ4PHW3ZTYAXYD263FTDSLMXBY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UZ4PHW3ZTYAXYD263FTDSLMXBY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UZ4PHW3ZTYAXYD263FTDSLMXBY/action/storage_attestation","attest_author":"https://pith.science/pith/UZ4PHW3ZTYAXYD263FTDSLMXBY/action/author_attestation","sign_citation":"https://pith.science/pith/UZ4PHW3ZTYAXYD263FTDSLMXBY/action/citation_signature","submit_replication":"https://pith.science/pith/UZ4PHW3ZTYAXYD263FTDSLMXBY/action/replication_record"}},"created_at":"2026-07-05T07:57:48.219156+00:00","updated_at":"2026-07-05T07:57:48.219156+00:00"}