{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AOREVZ4OZ5BIKY5DN3VAYF3BFY","short_pith_number":"pith:AOREVZ4O","schema_version":"1.0","canonical_sha256":"03a24ae78ecf428563a36eea0c17612e3d462eb4e6e0c80e6eef5d83b3c338db","source":{"kind":"arxiv","id":"2506.00365","version":1},"attestation_state":"computed","paper":{"title":"Feature Fusion and Knowledge-Distilled Multi-Modal Multi-Target Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.CV","authors_text":"Ngoc Tuyen Do, Tri Nhu Do","submitted_at":"2025-05-31T03:11:44Z","abstract_excerpt":"In the surveillance and defense domain, multi-target detection and classification (MTD) is considered essential yet challenging due to heterogeneous inputs from diverse data sources and the computational complexity of algorithms designed for resource-constrained embedded devices, particularly for Al-based solutions. To address these challenges, we propose a feature fusion and knowledge-distilled framework for multi-modal MTD that leverages data fusion to enhance accuracy and employs knowledge distillation for improved domain adaptation. Specifically, our approach utilizes both RGB and thermal "},"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":"2506.00365","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-31T03:11:44Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"c4e8a39710ca156e4246f4c3e9bb5def1f5d3d0d71a4d2497e5850094ac964c4","abstract_canon_sha256":"0c9d8a1b3ef9ee7af3c6f75290f9bf1ae3a5abfd48c6ba073049bab2f745532b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:33.249389Z","signature_b64":"ToPV88+55a1R3wDxnsFmmnEcZRm/3v+vAwMOAxHsKMGtNl+X4Hp+z/0NQdEsIUsmVqldLG4CCxkXDe/oL1nWCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03a24ae78ecf428563a36eea0c17612e3d462eb4e6e0c80e6eef5d83b3c338db","last_reissued_at":"2026-07-05T11:13:33.248964Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:33.248964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feature Fusion and Knowledge-Distilled Multi-Modal Multi-Target Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.CV","authors_text":"Ngoc Tuyen Do, Tri Nhu Do","submitted_at":"2025-05-31T03:11:44Z","abstract_excerpt":"In the surveillance and defense domain, multi-target detection and classification (MTD) is considered essential yet challenging due to heterogeneous inputs from diverse data sources and the computational complexity of algorithms designed for resource-constrained embedded devices, particularly for Al-based solutions. To address these challenges, we propose a feature fusion and knowledge-distilled framework for multi-modal MTD that leverages data fusion to enhance accuracy and employs knowledge distillation for improved domain adaptation. Specifically, our approach utilizes both RGB and thermal "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00365","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/2506.00365/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":"2506.00365","created_at":"2026-07-05T11:13:33.249023+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.00365v1","created_at":"2026-07-05T11:13:33.249023+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00365","created_at":"2026-07-05T11:13:33.249023+00:00"},{"alias_kind":"pith_short_12","alias_value":"AOREVZ4OZ5BI","created_at":"2026-07-05T11:13:33.249023+00:00"},{"alias_kind":"pith_short_16","alias_value":"AOREVZ4OZ5BIKY5D","created_at":"2026-07-05T11:13:33.249023+00:00"},{"alias_kind":"pith_short_8","alias_value":"AOREVZ4O","created_at":"2026-07-05T11:13:33.249023+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AOREVZ4OZ5BIKY5DN3VAYF3BFY","json":"https://pith.science/pith/AOREVZ4OZ5BIKY5DN3VAYF3BFY.json","graph_json":"https://pith.science/api/pith-number/AOREVZ4OZ5BIKY5DN3VAYF3BFY/graph.json","events_json":"https://pith.science/api/pith-number/AOREVZ4OZ5BIKY5DN3VAYF3BFY/events.json","paper":"https://pith.science/paper/AOREVZ4O"},"agent_actions":{"view_html":"https://pith.science/pith/AOREVZ4OZ5BIKY5DN3VAYF3BFY","download_json":"https://pith.science/pith/AOREVZ4OZ5BIKY5DN3VAYF3BFY.json","view_paper":"https://pith.science/paper/AOREVZ4O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.00365&json=true","fetch_graph":"https://pith.science/api/pith-number/AOREVZ4OZ5BIKY5DN3VAYF3BFY/graph.json","fetch_events":"https://pith.science/api/pith-number/AOREVZ4OZ5BIKY5DN3VAYF3BFY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AOREVZ4OZ5BIKY5DN3VAYF3BFY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AOREVZ4OZ5BIKY5DN3VAYF3BFY/action/storage_attestation","attest_author":"https://pith.science/pith/AOREVZ4OZ5BIKY5DN3VAYF3BFY/action/author_attestation","sign_citation":"https://pith.science/pith/AOREVZ4OZ5BIKY5DN3VAYF3BFY/action/citation_signature","submit_replication":"https://pith.science/pith/AOREVZ4OZ5BIKY5DN3VAYF3BFY/action/replication_record"}},"created_at":"2026-07-05T11:13:33.249023+00:00","updated_at":"2026-07-05T11:13:33.249023+00:00"}