{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PBQF5J6243Q72UGXF3O7TM4FLX","short_pith_number":"pith:PBQF5J62","schema_version":"1.0","canonical_sha256":"78605ea7dae6e1fd50d72eddf9b3855dea59fe7f10c6574b37b56b6f31beb13f","source":{"kind":"arxiv","id":"2310.00022","version":4},"attestation_state":"computed","paper":{"title":"CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mingming Zhang, Qingjie Liu, Yunhong Wang","submitted_at":"2023-09-28T18:04:43Z","abstract_excerpt":"Learning representations through self-supervision on unlabeled data has proven highly effective for understanding diverse images. However, remote sensing images often have complex and densely populated scenes with multiple land objects and no clear foreground objects. This intrinsic property generates high object density, resulting in false positive pairs or missing contextual information in self-supervised learning. To address these problems, we propose a context-enhanced masked image modeling method (CtxMIM), a simple yet efficient MIM-based self-supervised learning for remote sensing image "},"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":"2310.00022","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T18:04:43Z","cross_cats_sorted":[],"title_canon_sha256":"b4a70991ee536c942db988dffdb826f542adbb45dbb2eb7533faf981247d9583","abstract_canon_sha256":"d9b6706b2ad5941a6831441017f7775c91f58e68d422d822119ca11ee1750f2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:21:41.469546Z","signature_b64":"s9asuTSaKNyBMNiwbHPbvKRC6/xDi0XoSWxOUwrq/jtmJZnfU+3HBvCn4pBY0HRG+wBx0pZOsq9D6L5p6eIFBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78605ea7dae6e1fd50d72eddf9b3855dea59fe7f10c6574b37b56b6f31beb13f","last_reissued_at":"2026-07-05T08:21:41.469034Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:21:41.469034Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mingming Zhang, Qingjie Liu, Yunhong Wang","submitted_at":"2023-09-28T18:04:43Z","abstract_excerpt":"Learning representations through self-supervision on unlabeled data has proven highly effective for understanding diverse images. However, remote sensing images often have complex and densely populated scenes with multiple land objects and no clear foreground objects. This intrinsic property generates high object density, resulting in false positive pairs or missing contextual information in self-supervised learning. To address these problems, we propose a context-enhanced masked image modeling method (CtxMIM), a simple yet efficient MIM-based self-supervised learning for remote sensing image "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00022","kind":"arxiv","version":4},"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/2310.00022/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":"2310.00022","created_at":"2026-07-05T08:21:41.469097+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.00022v4","created_at":"2026-07-05T08:21:41.469097+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00022","created_at":"2026-07-05T08:21:41.469097+00:00"},{"alias_kind":"pith_short_12","alias_value":"PBQF5J6243Q7","created_at":"2026-07-05T08:21:41.469097+00:00"},{"alias_kind":"pith_short_16","alias_value":"PBQF5J6243Q72UGX","created_at":"2026-07-05T08:21:41.469097+00:00"},{"alias_kind":"pith_short_8","alias_value":"PBQF5J62","created_at":"2026-07-05T08:21:41.469097+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12542","citing_title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2506.20380","citing_title":"TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12542","citing_title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PBQF5J6243Q72UGXF3O7TM4FLX","json":"https://pith.science/pith/PBQF5J6243Q72UGXF3O7TM4FLX.json","graph_json":"https://pith.science/api/pith-number/PBQF5J6243Q72UGXF3O7TM4FLX/graph.json","events_json":"https://pith.science/api/pith-number/PBQF5J6243Q72UGXF3O7TM4FLX/events.json","paper":"https://pith.science/paper/PBQF5J62"},"agent_actions":{"view_html":"https://pith.science/pith/PBQF5J6243Q72UGXF3O7TM4FLX","download_json":"https://pith.science/pith/PBQF5J6243Q72UGXF3O7TM4FLX.json","view_paper":"https://pith.science/paper/PBQF5J62","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.00022&json=true","fetch_graph":"https://pith.science/api/pith-number/PBQF5J6243Q72UGXF3O7TM4FLX/graph.json","fetch_events":"https://pith.science/api/pith-number/PBQF5J6243Q72UGXF3O7TM4FLX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PBQF5J6243Q72UGXF3O7TM4FLX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PBQF5J6243Q72UGXF3O7TM4FLX/action/storage_attestation","attest_author":"https://pith.science/pith/PBQF5J6243Q72UGXF3O7TM4FLX/action/author_attestation","sign_citation":"https://pith.science/pith/PBQF5J6243Q72UGXF3O7TM4FLX/action/citation_signature","submit_replication":"https://pith.science/pith/PBQF5J6243Q72UGXF3O7TM4FLX/action/replication_record"}},"created_at":"2026-07-05T08:21:41.469097+00:00","updated_at":"2026-07-05T08:21:41.469097+00:00"}