{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ANQN2QMOPACX2QSSEH3PBIV2MT","short_pith_number":"pith:ANQN2QMO","schema_version":"1.0","canonical_sha256":"0360dd418e78057d425221f6f0a2ba64c60e1cbcaec5b54e97dcf3584edb7908","source":{"kind":"arxiv","id":"2410.23905","version":1},"attestation_state":"computed","paper":{"title":"Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Zhang, Jiayi Ma, Lei Cao","submitted_at":"2024-10-31T13:10:50Z","abstract_excerpt":"Existing multi-modal image fusion methods fail to address the compound degradations presented in source images, resulting in fusion images plagued by noise, color bias, improper exposure, \\textit{etc}. Additionally, these methods often overlook the specificity of foreground objects, weakening the salience of the objects of interest within the fused images. To address these challenges, this study proposes a novel interactive multi-modal image fusion framework based on the text-modulated diffusion model, called Text-DiFuse. First, this framework integrates feature-level information integration i"},"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":"2410.23905","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-31T13:10:50Z","cross_cats_sorted":[],"title_canon_sha256":"3738183b3b26486003e6cbb65ac42a207fbf24932523ec54308283e997f2bf31","abstract_canon_sha256":"473403d4a6ba74399d0a4cc2d2c0a7c5c2916898b9f5992460c51740ca53edea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:29:07.099268Z","signature_b64":"ApFyGwnS4HvjKPcPqImgYLMeD+mcCpHAT6/ExIlgUcGJKLCeiqZ9pmDVWn01PcOoPBhLL4BzNtsk4ZcUPjnNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0360dd418e78057d425221f6f0a2ba64c60e1cbcaec5b54e97dcf3584edb7908","last_reissued_at":"2026-07-05T09:29:07.098698Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:29:07.098698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Zhang, Jiayi Ma, Lei Cao","submitted_at":"2024-10-31T13:10:50Z","abstract_excerpt":"Existing multi-modal image fusion methods fail to address the compound degradations presented in source images, resulting in fusion images plagued by noise, color bias, improper exposure, \\textit{etc}. Additionally, these methods often overlook the specificity of foreground objects, weakening the salience of the objects of interest within the fused images. To address these challenges, this study proposes a novel interactive multi-modal image fusion framework based on the text-modulated diffusion model, called Text-DiFuse. First, this framework integrates feature-level information integration i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23905","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/2410.23905/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":"2410.23905","created_at":"2026-07-05T09:29:07.098770+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.23905v1","created_at":"2026-07-05T09:29:07.098770+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23905","created_at":"2026-07-05T09:29:07.098770+00:00"},{"alias_kind":"pith_short_12","alias_value":"ANQN2QMOPACX","created_at":"2026-07-05T09:29:07.098770+00:00"},{"alias_kind":"pith_short_16","alias_value":"ANQN2QMOPACX2QSS","created_at":"2026-07-05T09:29:07.098770+00:00"},{"alias_kind":"pith_short_8","alias_value":"ANQN2QMO","created_at":"2026-07-05T09:29:07.098770+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.17795","citing_title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ANQN2QMOPACX2QSSEH3PBIV2MT","json":"https://pith.science/pith/ANQN2QMOPACX2QSSEH3PBIV2MT.json","graph_json":"https://pith.science/api/pith-number/ANQN2QMOPACX2QSSEH3PBIV2MT/graph.json","events_json":"https://pith.science/api/pith-number/ANQN2QMOPACX2QSSEH3PBIV2MT/events.json","paper":"https://pith.science/paper/ANQN2QMO"},"agent_actions":{"view_html":"https://pith.science/pith/ANQN2QMOPACX2QSSEH3PBIV2MT","download_json":"https://pith.science/pith/ANQN2QMOPACX2QSSEH3PBIV2MT.json","view_paper":"https://pith.science/paper/ANQN2QMO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.23905&json=true","fetch_graph":"https://pith.science/api/pith-number/ANQN2QMOPACX2QSSEH3PBIV2MT/graph.json","fetch_events":"https://pith.science/api/pith-number/ANQN2QMOPACX2QSSEH3PBIV2MT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ANQN2QMOPACX2QSSEH3PBIV2MT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ANQN2QMOPACX2QSSEH3PBIV2MT/action/storage_attestation","attest_author":"https://pith.science/pith/ANQN2QMOPACX2QSSEH3PBIV2MT/action/author_attestation","sign_citation":"https://pith.science/pith/ANQN2QMOPACX2QSSEH3PBIV2MT/action/citation_signature","submit_replication":"https://pith.science/pith/ANQN2QMOPACX2QSSEH3PBIV2MT/action/replication_record"}},"created_at":"2026-07-05T09:29:07.098770+00:00","updated_at":"2026-07-05T09:29:07.098770+00:00"}