{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XBZKRP2K6TABKCRVWOUM4K4MMQ","short_pith_number":"pith:XBZKRP2K","schema_version":"1.0","canonical_sha256":"b872a8bf4af4c0150a35b3a8ce2b8c640b0db2ece78606b83d07f71d61430ace","source":{"kind":"arxiv","id":"2603.14412","version":2},"attestation_state":"computed","paper":{"title":"G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingze Liang, Liang-Jian Deng, Shan Yin, Zhiqi Yang","submitted_at":"2026-03-15T14:55:46Z","abstract_excerpt":"Pansharpening aims to fuse a high-resolution panchromatic (PAN) image and a low-resolution multispectral (LRMS) image to produce a high-resolution multispectral (HRMS) image. Recent deep models have achieved strong performance, yet they typically rely on large-scale pretraining and often generalize poorly to unseen real-world image pairs. Prior zero-shot approaches improve real-scene generalization but require per-image optimization, hindering weight reuse, and the above methods are usually limited to a fixed scale. To address this issue, we propose G-ZAP, a generalizable zero-shot framework f"},"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":"2603.14412","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-03-15T14:55:46Z","cross_cats_sorted":[],"title_canon_sha256":"1939e629dc372f0f525461767a1aa5cd364061e29065592e8efc468879dbde6a","abstract_canon_sha256":"6070aa645b8e65610be6ebfeaee46ad1880f318458a5ddade568c9931182d60d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-09T01:19:52.920742Z","signature_b64":"PBjeSqOPVHhbkwhJW14fK7zaVu/0SARqZZUZC/2hNAH7LDzFO6yDkqjtKRLG/gRVph1F2vvW0RKgP6N99ZIDCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b872a8bf4af4c0150a35b3a8ce2b8c640b0db2ece78606b83d07f71d61430ace","last_reissued_at":"2026-07-09T01:19:52.920177Z","signature_status":"signed_v1","first_computed_at":"2026-07-09T01:19:52.920177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingze Liang, Liang-Jian Deng, Shan Yin, Zhiqi Yang","submitted_at":"2026-03-15T14:55:46Z","abstract_excerpt":"Pansharpening aims to fuse a high-resolution panchromatic (PAN) image and a low-resolution multispectral (LRMS) image to produce a high-resolution multispectral (HRMS) image. Recent deep models have achieved strong performance, yet they typically rely on large-scale pretraining and often generalize poorly to unseen real-world image pairs. Prior zero-shot approaches improve real-scene generalization but require per-image optimization, hindering weight reuse, and the above methods are usually limited to a fixed scale. To address this issue, we propose G-ZAP, a generalizable zero-shot framework f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.14412","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/2603.14412/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":"2603.14412","created_at":"2026-07-09T01:19:52.920236+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.14412v2","created_at":"2026-07-09T01:19:52.920236+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.14412","created_at":"2026-07-09T01:19:52.920236+00:00"},{"alias_kind":"pith_short_12","alias_value":"XBZKRP2K6TAB","created_at":"2026-07-09T01:19:52.920236+00:00"},{"alias_kind":"pith_short_16","alias_value":"XBZKRP2K6TABKCRV","created_at":"2026-07-09T01:19:52.920236+00:00"},{"alias_kind":"pith_short_8","alias_value":"XBZKRP2K","created_at":"2026-07-09T01:19:52.920236+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.17722","citing_title":"GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XBZKRP2K6TABKCRVWOUM4K4MMQ","json":"https://pith.science/pith/XBZKRP2K6TABKCRVWOUM4K4MMQ.json","graph_json":"https://pith.science/api/pith-number/XBZKRP2K6TABKCRVWOUM4K4MMQ/graph.json","events_json":"https://pith.science/api/pith-number/XBZKRP2K6TABKCRVWOUM4K4MMQ/events.json","paper":"https://pith.science/paper/XBZKRP2K"},"agent_actions":{"view_html":"https://pith.science/pith/XBZKRP2K6TABKCRVWOUM4K4MMQ","download_json":"https://pith.science/pith/XBZKRP2K6TABKCRVWOUM4K4MMQ.json","view_paper":"https://pith.science/paper/XBZKRP2K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.14412&json=true","fetch_graph":"https://pith.science/api/pith-number/XBZKRP2K6TABKCRVWOUM4K4MMQ/graph.json","fetch_events":"https://pith.science/api/pith-number/XBZKRP2K6TABKCRVWOUM4K4MMQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XBZKRP2K6TABKCRVWOUM4K4MMQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XBZKRP2K6TABKCRVWOUM4K4MMQ/action/storage_attestation","attest_author":"https://pith.science/pith/XBZKRP2K6TABKCRVWOUM4K4MMQ/action/author_attestation","sign_citation":"https://pith.science/pith/XBZKRP2K6TABKCRVWOUM4K4MMQ/action/citation_signature","submit_replication":"https://pith.science/pith/XBZKRP2K6TABKCRVWOUM4K4MMQ/action/replication_record"}},"created_at":"2026-07-09T01:19:52.920236+00:00","updated_at":"2026-07-09T01:19:52.920236+00:00"}