{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AHMOVO6OQVLTRRS6NJWDXNCYZF","short_pith_number":"pith:AHMOVO6O","schema_version":"1.0","canonical_sha256":"01d8eabbce855738c65e6a6c3bb458c946e89d75a6ae5845f5a31f9854a697d9","source":{"kind":"arxiv","id":"2506.21018","version":1},"attestation_state":"computed","paper":{"title":"LASFNet: A Lightweight Attention-Guided Self-Modulation Feature Fusion Network for Multimodal Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chang Liu, Lei Hao, Lina Xu, Yanni Dong","submitted_at":"2025-06-26T05:32:33Z","abstract_excerpt":"Effective deep feature extraction via feature-level fusion is crucial for multimodal object detection. However, previous studies often involve complex training processes that integrate modality-specific features by stacking multiple feature-level fusion units, leading to significant computational overhead. To address this issue, we propose a new fusion detection baseline that uses a single feature-level fusion unit to enable high-performance detection, thereby simplifying the training process. Based on this approach, we propose a lightweight attention-guided self-modulation feature fusion netw"},"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":"2506.21018","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-26T05:32:33Z","cross_cats_sorted":[],"title_canon_sha256":"7774aa55061b0b6cae93d95d7b446e34243ca6042714296450da582c1413d2dc","abstract_canon_sha256":"05e773f70b7ba91d2e9fe5f46088405166d4b0588cd282367a9126f604ab7398"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:37.927501Z","signature_b64":"VYL7w1n1FhnqV6TlED0xoL/9AFDD0JUEei/kjclATFkELcyAYFGliofDGsXcRyanHrMO0GOqlKS55MG5v67lBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01d8eabbce855738c65e6a6c3bb458c946e89d75a6ae5845f5a31f9854a697d9","last_reissued_at":"2026-07-05T11:27:37.927067Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:37.927067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LASFNet: A Lightweight Attention-Guided Self-Modulation Feature Fusion Network for Multimodal Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chang Liu, Lei Hao, Lina Xu, Yanni Dong","submitted_at":"2025-06-26T05:32:33Z","abstract_excerpt":"Effective deep feature extraction via feature-level fusion is crucial for multimodal object detection. However, previous studies often involve complex training processes that integrate modality-specific features by stacking multiple feature-level fusion units, leading to significant computational overhead. To address this issue, we propose a new fusion detection baseline that uses a single feature-level fusion unit to enable high-performance detection, thereby simplifying the training process. Based on this approach, we propose a lightweight attention-guided self-modulation feature fusion netw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21018","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/2506.21018/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":"2506.21018","created_at":"2026-07-05T11:27:37.927126+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.21018v1","created_at":"2026-07-05T11:27:37.927126+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21018","created_at":"2026-07-05T11:27:37.927126+00:00"},{"alias_kind":"pith_short_12","alias_value":"AHMOVO6OQVLT","created_at":"2026-07-05T11:27:37.927126+00:00"},{"alias_kind":"pith_short_16","alias_value":"AHMOVO6OQVLTRRS6","created_at":"2026-07-05T11:27:37.927126+00:00"},{"alias_kind":"pith_short_8","alias_value":"AHMOVO6O","created_at":"2026-07-05T11:27:37.927126+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/AHMOVO6OQVLTRRS6NJWDXNCYZF","json":"https://pith.science/pith/AHMOVO6OQVLTRRS6NJWDXNCYZF.json","graph_json":"https://pith.science/api/pith-number/AHMOVO6OQVLTRRS6NJWDXNCYZF/graph.json","events_json":"https://pith.science/api/pith-number/AHMOVO6OQVLTRRS6NJWDXNCYZF/events.json","paper":"https://pith.science/paper/AHMOVO6O"},"agent_actions":{"view_html":"https://pith.science/pith/AHMOVO6OQVLTRRS6NJWDXNCYZF","download_json":"https://pith.science/pith/AHMOVO6OQVLTRRS6NJWDXNCYZF.json","view_paper":"https://pith.science/paper/AHMOVO6O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.21018&json=true","fetch_graph":"https://pith.science/api/pith-number/AHMOVO6OQVLTRRS6NJWDXNCYZF/graph.json","fetch_events":"https://pith.science/api/pith-number/AHMOVO6OQVLTRRS6NJWDXNCYZF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AHMOVO6OQVLTRRS6NJWDXNCYZF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AHMOVO6OQVLTRRS6NJWDXNCYZF/action/storage_attestation","attest_author":"https://pith.science/pith/AHMOVO6OQVLTRRS6NJWDXNCYZF/action/author_attestation","sign_citation":"https://pith.science/pith/AHMOVO6OQVLTRRS6NJWDXNCYZF/action/citation_signature","submit_replication":"https://pith.science/pith/AHMOVO6OQVLTRRS6NJWDXNCYZF/action/replication_record"}},"created_at":"2026-07-05T11:27:37.927126+00:00","updated_at":"2026-07-05T11:27:37.927126+00:00"}