{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JOKZYZML27IIPKXPISWSQY2QDO","short_pith_number":"pith:JOKZYZML","schema_version":"1.0","canonical_sha256":"4b959c658bd7d087aaef44ad2863501b974618a710c89f3f8ae07824cf1bf761","source":{"kind":"arxiv","id":"2506.23077","version":1},"attestation_state":"computed","paper":{"title":"Dynamic Contrastive Learning for Hierarchical Retrieval: A Case Study of Distance-Aware Cross-View Geo-Localization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haifeng Hu, Quan Zhou, Suofei Zhang, Xiaofu Wu, Xinxin Wang","submitted_at":"2025-06-29T03:57:01Z","abstract_excerpt":"Existing deep learning-based cross-view geo-localization methods primarily focus on improving the accuracy of cross-domain image matching, rather than enabling models to comprehensively capture contextual information around the target and minimize the cost of localization errors. To support systematic research into this Distance-Aware Cross-View Geo-Localization (DACVGL) problem, we construct Distance-Aware Campus (DA-Campus), the first benchmark that pairs multi-view imagery with precise distance annotations across three spatial resolutions. Based on DA-Campus, we formulate DACVGL as a hierar"},"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":"2506.23077","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-29T03:57:01Z","cross_cats_sorted":[],"title_canon_sha256":"b382a9c574e5b096e5593457e0216cb6b0db9e40fa6e88a5a77a0024069b9895","abstract_canon_sha256":"45e8bcb3781d542e9766c722ca7436dbe19d885a2c04591fb61ab605930635e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:50.504785Z","signature_b64":"HMvDccJwzkn0XIfge6kmDVOXJYIxhLTb9yOd8xfBvqP62CHEmAfMQNEg3EBOlS3rCqPvFxggdfz4eg4K8oKfAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b959c658bd7d087aaef44ad2863501b974618a710c89f3f8ae07824cf1bf761","last_reissued_at":"2026-07-05T11:28:50.504251Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:50.504251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Contrastive Learning for Hierarchical Retrieval: A Case Study of Distance-Aware Cross-View Geo-Localization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haifeng Hu, Quan Zhou, Suofei Zhang, Xiaofu Wu, Xinxin Wang","submitted_at":"2025-06-29T03:57:01Z","abstract_excerpt":"Existing deep learning-based cross-view geo-localization methods primarily focus on improving the accuracy of cross-domain image matching, rather than enabling models to comprehensively capture contextual information around the target and minimize the cost of localization errors. To support systematic research into this Distance-Aware Cross-View Geo-Localization (DACVGL) problem, we construct Distance-Aware Campus (DA-Campus), the first benchmark that pairs multi-view imagery with precise distance annotations across three spatial resolutions. Based on DA-Campus, we formulate DACVGL as a hierar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23077","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/2506.23077/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":"2506.23077","created_at":"2026-07-05T11:28:50.504321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.23077v1","created_at":"2026-07-05T11:28:50.504321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23077","created_at":"2026-07-05T11:28:50.504321+00:00"},{"alias_kind":"pith_short_12","alias_value":"JOKZYZML27II","created_at":"2026-07-05T11:28:50.504321+00:00"},{"alias_kind":"pith_short_16","alias_value":"JOKZYZML27IIPKXP","created_at":"2026-07-05T11:28:50.504321+00:00"},{"alias_kind":"pith_short_8","alias_value":"JOKZYZML","created_at":"2026-07-05T11:28:50.504321+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/JOKZYZML27IIPKXPISWSQY2QDO","json":"https://pith.science/pith/JOKZYZML27IIPKXPISWSQY2QDO.json","graph_json":"https://pith.science/api/pith-number/JOKZYZML27IIPKXPISWSQY2QDO/graph.json","events_json":"https://pith.science/api/pith-number/JOKZYZML27IIPKXPISWSQY2QDO/events.json","paper":"https://pith.science/paper/JOKZYZML"},"agent_actions":{"view_html":"https://pith.science/pith/JOKZYZML27IIPKXPISWSQY2QDO","download_json":"https://pith.science/pith/JOKZYZML27IIPKXPISWSQY2QDO.json","view_paper":"https://pith.science/paper/JOKZYZML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.23077&json=true","fetch_graph":"https://pith.science/api/pith-number/JOKZYZML27IIPKXPISWSQY2QDO/graph.json","fetch_events":"https://pith.science/api/pith-number/JOKZYZML27IIPKXPISWSQY2QDO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JOKZYZML27IIPKXPISWSQY2QDO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JOKZYZML27IIPKXPISWSQY2QDO/action/storage_attestation","attest_author":"https://pith.science/pith/JOKZYZML27IIPKXPISWSQY2QDO/action/author_attestation","sign_citation":"https://pith.science/pith/JOKZYZML27IIPKXPISWSQY2QDO/action/citation_signature","submit_replication":"https://pith.science/pith/JOKZYZML27IIPKXPISWSQY2QDO/action/replication_record"}},"created_at":"2026-07-05T11:28:50.504321+00:00","updated_at":"2026-07-05T11:28:50.504321+00:00"}