{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:APL6HDWPJQ5D3XMSR2A7NKGTLL","short_pith_number":"pith:APL6HDWP","schema_version":"1.0","canonical_sha256":"03d7e38ecf4c3a3ddd928e81f6a8d35ac18773b26e72be700d79250d86bf49e4","source":{"kind":"arxiv","id":"2606.08918","version":1},"attestation_state":"computed","paper":{"title":"When Vision Misleads, Let Location Speak: A Worldwide Image Geo-Localization Method via Location Attention Mechanism and Large Multimodal Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junchao Cui, Nan Wu, Shaoyong Du, Wenqi Shi, Xiangyang Luo, Xuanzi Ma","submitted_at":"2026-06-08T01:49:44Z","abstract_excerpt":"Worldwide image geo-localization aims to determine the capture location of an image on a global scale. Existing methods often mislocalize images by matching them to visually similar scenes from different geographic regions, which limits reliability in practical applications. To address this issue, we propose TransGeoCLIP, a novel retrieval-based framework that integrates a location attention mechanism and large multimodal models (LMMs). Using the Transformer encoder with location attention to encode GPS coordinates, TransGeoCLIP can effectively distinguish geographic features among visually si"},"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":"2606.08918","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-08T01:49:44Z","cross_cats_sorted":[],"title_canon_sha256":"b55904d55f6738fdebfa6baf96d1c20f518b5820924d07e39ee0e13b0cedbd46","abstract_canon_sha256":"5bb641023b8a14874447840c81bea0ffc5a9110b7fe7f1fdfa2949d96b3443e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:47.273465Z","signature_b64":"l1ZKqj9wDC8Xf5ntR737mqNofjKvj0ExuV0Dc/Lqz4gWDJBQzFPFty/hb8RjxquI8yOn7gwg8eQaoBHjusQhAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03d7e38ecf4c3a3ddd928e81f6a8d35ac18773b26e72be700d79250d86bf49e4","last_reissued_at":"2026-06-09T02:07:47.272556Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:47.272556Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Vision Misleads, Let Location Speak: A Worldwide Image Geo-Localization Method via Location Attention Mechanism and Large Multimodal Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junchao Cui, Nan Wu, Shaoyong Du, Wenqi Shi, Xiangyang Luo, Xuanzi Ma","submitted_at":"2026-06-08T01:49:44Z","abstract_excerpt":"Worldwide image geo-localization aims to determine the capture location of an image on a global scale. Existing methods often mislocalize images by matching them to visually similar scenes from different geographic regions, which limits reliability in practical applications. To address this issue, we propose TransGeoCLIP, a novel retrieval-based framework that integrates a location attention mechanism and large multimodal models (LMMs). Using the Transformer encoder with location attention to encode GPS coordinates, TransGeoCLIP can effectively distinguish geographic features among visually si"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08918","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/2606.08918/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":"2606.08918","created_at":"2026-06-09T02:07:47.272710+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.08918v1","created_at":"2026-06-09T02:07:47.272710+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08918","created_at":"2026-06-09T02:07:47.272710+00:00"},{"alias_kind":"pith_short_12","alias_value":"APL6HDWPJQ5D","created_at":"2026-06-09T02:07:47.272710+00:00"},{"alias_kind":"pith_short_16","alias_value":"APL6HDWPJQ5D3XMS","created_at":"2026-06-09T02:07:47.272710+00:00"},{"alias_kind":"pith_short_8","alias_value":"APL6HDWP","created_at":"2026-06-09T02:07:47.272710+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/APL6HDWPJQ5D3XMSR2A7NKGTLL","json":"https://pith.science/pith/APL6HDWPJQ5D3XMSR2A7NKGTLL.json","graph_json":"https://pith.science/api/pith-number/APL6HDWPJQ5D3XMSR2A7NKGTLL/graph.json","events_json":"https://pith.science/api/pith-number/APL6HDWPJQ5D3XMSR2A7NKGTLL/events.json","paper":"https://pith.science/paper/APL6HDWP"},"agent_actions":{"view_html":"https://pith.science/pith/APL6HDWPJQ5D3XMSR2A7NKGTLL","download_json":"https://pith.science/pith/APL6HDWPJQ5D3XMSR2A7NKGTLL.json","view_paper":"https://pith.science/paper/APL6HDWP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.08918&json=true","fetch_graph":"https://pith.science/api/pith-number/APL6HDWPJQ5D3XMSR2A7NKGTLL/graph.json","fetch_events":"https://pith.science/api/pith-number/APL6HDWPJQ5D3XMSR2A7NKGTLL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/APL6HDWPJQ5D3XMSR2A7NKGTLL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/APL6HDWPJQ5D3XMSR2A7NKGTLL/action/storage_attestation","attest_author":"https://pith.science/pith/APL6HDWPJQ5D3XMSR2A7NKGTLL/action/author_attestation","sign_citation":"https://pith.science/pith/APL6HDWPJQ5D3XMSR2A7NKGTLL/action/citation_signature","submit_replication":"https://pith.science/pith/APL6HDWPJQ5D3XMSR2A7NKGTLL/action/replication_record"}},"created_at":"2026-06-09T02:07:47.272710+00:00","updated_at":"2026-06-09T02:07:47.272710+00:00"}