{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GQVQXSWUGMFRU6POOB4TLFE2KF","short_pith_number":"pith:GQVQXSWU","schema_version":"1.0","canonical_sha256":"342b0bcad4330b1a79ee707935949a5177af0e2d30cdbc4c12c5922abb569eea","source":{"kind":"arxiv","id":"2409.17993","version":5},"attestation_state":"computed","paper":{"title":"SSHNet: Unsupervised Cross-modal Homography Estimation via Problem Reformulation and Split Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bailin Yang, Chenghao Zhang, Hui-Liang Shen, Junchen Yu, Runmin Zhang, Shujie Chen, Si-Yuan Cao, Zhu Yu","submitted_at":"2024-09-26T16:04:31Z","abstract_excerpt":"We propose a novel unsupervised cross-modal homography estimation learning framework, named Split Supervised Homography estimation Network (SSHNet). SSHNet reformulates the unsupervised cross-modal homography estimation into two supervised sub-problems, each addressed by its specialized network: a homography estimation network and a modality transfer network. To realize stable training, we introduce an effective split optimization strategy to train each network separately within its respective sub-problem. We also formulate an extra homography feature space supervision to enhance feature consi"},"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":"2409.17993","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-26T16:04:31Z","cross_cats_sorted":[],"title_canon_sha256":"be1ff8e1941f989dea077b49bdf6eea86956b8454808535c3286196cad13ba76","abstract_canon_sha256":"6c3282b4e743d90140678686f643a0ac926a74c16eeb97c56d27c31e8c910ad4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:36.950909Z","signature_b64":"j1ULG+gFdxhxW4SEUaMODA0NOWxHIsL74hF31Gmyi3OCIZeQ4PJ7vecboN+ZKZ4GuKIBgfJIUiyTCuC61+5rBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"342b0bcad4330b1a79ee707935949a5177af0e2d30cdbc4c12c5922abb569eea","last_reissued_at":"2026-07-05T10:54:36.950416Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:36.950416Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SSHNet: Unsupervised Cross-modal Homography Estimation via Problem Reformulation and Split Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bailin Yang, Chenghao Zhang, Hui-Liang Shen, Junchen Yu, Runmin Zhang, Shujie Chen, Si-Yuan Cao, Zhu Yu","submitted_at":"2024-09-26T16:04:31Z","abstract_excerpt":"We propose a novel unsupervised cross-modal homography estimation learning framework, named Split Supervised Homography estimation Network (SSHNet). SSHNet reformulates the unsupervised cross-modal homography estimation into two supervised sub-problems, each addressed by its specialized network: a homography estimation network and a modality transfer network. To realize stable training, we introduce an effective split optimization strategy to train each network separately within its respective sub-problem. We also formulate an extra homography feature space supervision to enhance feature consi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17993","kind":"arxiv","version":5},"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/2409.17993/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":"2409.17993","created_at":"2026-07-05T10:54:36.950484+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17993v5","created_at":"2026-07-05T10:54:36.950484+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17993","created_at":"2026-07-05T10:54:36.950484+00:00"},{"alias_kind":"pith_short_12","alias_value":"GQVQXSWUGMFR","created_at":"2026-07-05T10:54:36.950484+00:00"},{"alias_kind":"pith_short_16","alias_value":"GQVQXSWUGMFRU6PO","created_at":"2026-07-05T10:54:36.950484+00:00"},{"alias_kind":"pith_short_8","alias_value":"GQVQXSWU","created_at":"2026-07-05T10:54:36.950484+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.22000","citing_title":"Collaborative Learning for Unsupervised Multimodal Remote Sensing Image Registration: Integrating Self-Supervision and MIM-Guided Diffusion-Based Image Translation","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GQVQXSWUGMFRU6POOB4TLFE2KF","json":"https://pith.science/pith/GQVQXSWUGMFRU6POOB4TLFE2KF.json","graph_json":"https://pith.science/api/pith-number/GQVQXSWUGMFRU6POOB4TLFE2KF/graph.json","events_json":"https://pith.science/api/pith-number/GQVQXSWUGMFRU6POOB4TLFE2KF/events.json","paper":"https://pith.science/paper/GQVQXSWU"},"agent_actions":{"view_html":"https://pith.science/pith/GQVQXSWUGMFRU6POOB4TLFE2KF","download_json":"https://pith.science/pith/GQVQXSWUGMFRU6POOB4TLFE2KF.json","view_paper":"https://pith.science/paper/GQVQXSWU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17993&json=true","fetch_graph":"https://pith.science/api/pith-number/GQVQXSWUGMFRU6POOB4TLFE2KF/graph.json","fetch_events":"https://pith.science/api/pith-number/GQVQXSWUGMFRU6POOB4TLFE2KF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GQVQXSWUGMFRU6POOB4TLFE2KF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GQVQXSWUGMFRU6POOB4TLFE2KF/action/storage_attestation","attest_author":"https://pith.science/pith/GQVQXSWUGMFRU6POOB4TLFE2KF/action/author_attestation","sign_citation":"https://pith.science/pith/GQVQXSWUGMFRU6POOB4TLFE2KF/action/citation_signature","submit_replication":"https://pith.science/pith/GQVQXSWUGMFRU6POOB4TLFE2KF/action/replication_record"}},"created_at":"2026-07-05T10:54:36.950484+00:00","updated_at":"2026-07-05T10:54:36.950484+00:00"}