{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QRLF3E6GY3WEQXRB3ODNEMCKIX","short_pith_number":"pith:QRLF3E6G","schema_version":"1.0","canonical_sha256":"84565d93c6c6ec485e21db86d2304a45c15e6ce47c260145b3a9caed4bffc938","source":{"kind":"arxiv","id":"2504.02801","version":1},"attestation_state":"computed","paper":{"title":"F-ViTA: Foundation Model Guided Visible to Thermal Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Celso de Melo, Jay N. Paranjape, Vishal M. Patel","submitted_at":"2025-04-03T17:47:06Z","abstract_excerpt":"Thermal imaging is crucial for scene understanding, particularly in low-light and nighttime conditions. However, collecting large thermal datasets is costly and labor-intensive due to the specialized equipment required for infrared image capture. To address this challenge, researchers have explored visible-to-thermal image translation. Most existing methods rely on Generative Adversarial Networks (GANs) or Diffusion Models (DMs), treating the task as a style transfer problem. As a result, these approaches attempt to learn both the modality distribution shift and underlying physical principles "},"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":"2504.02801","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-03T17:47:06Z","cross_cats_sorted":[],"title_canon_sha256":"b91bdac001c73b35253488809115feec655948133ed02e9030b8e981a1a1ce6f","abstract_canon_sha256":"f9b6a9c67d4a79cec6e12c52bebfd7de72e91798dd90b2ad0d0c63c25363bad5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:44:02.551684Z","signature_b64":"jtLHsw8l+oVyjEUZh7QQKIeNtvYw00cZxIYh+d/MiU/96Jx6EM2PTE7lf9EPvmONSyzH0/tQsh9MoIm9tj7fCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84565d93c6c6ec485e21db86d2304a45c15e6ce47c260145b3a9caed4bffc938","last_reissued_at":"2026-07-05T10:44:02.551240Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:44:02.551240Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"F-ViTA: Foundation Model Guided Visible to Thermal Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Celso de Melo, Jay N. Paranjape, Vishal M. Patel","submitted_at":"2025-04-03T17:47:06Z","abstract_excerpt":"Thermal imaging is crucial for scene understanding, particularly in low-light and nighttime conditions. However, collecting large thermal datasets is costly and labor-intensive due to the specialized equipment required for infrared image capture. To address this challenge, researchers have explored visible-to-thermal image translation. Most existing methods rely on Generative Adversarial Networks (GANs) or Diffusion Models (DMs), treating the task as a style transfer problem. As a result, these approaches attempt to learn both the modality distribution shift and underlying physical principles "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.02801","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/2504.02801/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":"2504.02801","created_at":"2026-07-05T10:44:02.551297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.02801v1","created_at":"2026-07-05T10:44:02.551297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.02801","created_at":"2026-07-05T10:44:02.551297+00:00"},{"alias_kind":"pith_short_12","alias_value":"QRLF3E6GY3WE","created_at":"2026-07-05T10:44:02.551297+00:00"},{"alias_kind":"pith_short_16","alias_value":"QRLF3E6GY3WEQXRB","created_at":"2026-07-05T10:44:02.551297+00:00"},{"alias_kind":"pith_short_8","alias_value":"QRLF3E6G","created_at":"2026-07-05T10:44:02.551297+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.21882","citing_title":"Thermo-VL: Extending Vision-Language Models to Thermal Infrared Perception","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10130","citing_title":"Thermal-Det: Language-Guided Cross-Modal Distillation for Open-Vocabulary Thermal Object Detection","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QRLF3E6GY3WEQXRB3ODNEMCKIX","json":"https://pith.science/pith/QRLF3E6GY3WEQXRB3ODNEMCKIX.json","graph_json":"https://pith.science/api/pith-number/QRLF3E6GY3WEQXRB3ODNEMCKIX/graph.json","events_json":"https://pith.science/api/pith-number/QRLF3E6GY3WEQXRB3ODNEMCKIX/events.json","paper":"https://pith.science/paper/QRLF3E6G"},"agent_actions":{"view_html":"https://pith.science/pith/QRLF3E6GY3WEQXRB3ODNEMCKIX","download_json":"https://pith.science/pith/QRLF3E6GY3WEQXRB3ODNEMCKIX.json","view_paper":"https://pith.science/paper/QRLF3E6G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.02801&json=true","fetch_graph":"https://pith.science/api/pith-number/QRLF3E6GY3WEQXRB3ODNEMCKIX/graph.json","fetch_events":"https://pith.science/api/pith-number/QRLF3E6GY3WEQXRB3ODNEMCKIX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QRLF3E6GY3WEQXRB3ODNEMCKIX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QRLF3E6GY3WEQXRB3ODNEMCKIX/action/storage_attestation","attest_author":"https://pith.science/pith/QRLF3E6GY3WEQXRB3ODNEMCKIX/action/author_attestation","sign_citation":"https://pith.science/pith/QRLF3E6GY3WEQXRB3ODNEMCKIX/action/citation_signature","submit_replication":"https://pith.science/pith/QRLF3E6GY3WEQXRB3ODNEMCKIX/action/replication_record"}},"created_at":"2026-07-05T10:44:02.551297+00:00","updated_at":"2026-07-05T10:44:02.551297+00:00"}