{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:J7EZUN37YRK5XNGCSEJ6GAP5ZQ","short_pith_number":"pith:J7EZUN37","schema_version":"1.0","canonical_sha256":"4fc99a377fc455dbb4c29113e301fdcc0d8736a0abae31bf804b5a4caa54bc68","source":{"kind":"arxiv","id":"2502.04656","version":2},"attestation_state":"computed","paper":{"title":"MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for accurate object detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haigen Hu, Haixia Long, Qiu Guan, Sheng Lian, Xinli Xu, Ying Tang, Zhiqiang Yang, Zhongwen Yu","submitted_at":"2025-02-07T04:49:42Z","abstract_excerpt":"Due to the effective multi-scale feature fusion capabilities of the Path Aggregation FPN (PAFPN), it has become a widely adopted component in YOLO-based detectors. However, PAFPN struggles to integrate high-level semantic cues with low-level spatial details, limiting its performance in real-world applications, especially with significant scale variations. In this paper, we propose MHAF-YOLO, a novel detection framework featuring a versatile neck design called the Multi-Branch Auxiliary FPN (MAFPN), which consists of two key modules: the Superficial Assisted Fusion (SAF) and Advanced Assisted F"},"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":"2502.04656","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-07T04:49:42Z","cross_cats_sorted":[],"title_canon_sha256":"546c4ec9a05df4635ab7da7a9df921ba510eb53c9d16d59100b0f7fd7654bdf4","abstract_canon_sha256":"dc996fe5e923e3a673885975d97527439384f59675a216c0ce434de92049655b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:57.267960Z","signature_b64":"yHxwumaX6KCwquhydhzxbVwIc+VS/BR7EAHR+0wfztnyUYjdbdOTtTUz6pvLAhnWVMVw5fqjtnKY98zJT/hzAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4fc99a377fc455dbb4c29113e301fdcc0d8736a0abae31bf804b5a4caa54bc68","last_reissued_at":"2026-07-05T10:19:57.267475Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:57.267475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for accurate object detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haigen Hu, Haixia Long, Qiu Guan, Sheng Lian, Xinli Xu, Ying Tang, Zhiqiang Yang, Zhongwen Yu","submitted_at":"2025-02-07T04:49:42Z","abstract_excerpt":"Due to the effective multi-scale feature fusion capabilities of the Path Aggregation FPN (PAFPN), it has become a widely adopted component in YOLO-based detectors. However, PAFPN struggles to integrate high-level semantic cues with low-level spatial details, limiting its performance in real-world applications, especially with significant scale variations. In this paper, we propose MHAF-YOLO, a novel detection framework featuring a versatile neck design called the Multi-Branch Auxiliary FPN (MAFPN), which consists of two key modules: the Superficial Assisted Fusion (SAF) and Advanced Assisted F"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.04656","kind":"arxiv","version":2},"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/2502.04656/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":"2502.04656","created_at":"2026-07-05T10:19:57.267532+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.04656v2","created_at":"2026-07-05T10:19:57.267532+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.04656","created_at":"2026-07-05T10:19:57.267532+00:00"},{"alias_kind":"pith_short_12","alias_value":"J7EZUN37YRK5","created_at":"2026-07-05T10:19:57.267532+00:00"},{"alias_kind":"pith_short_16","alias_value":"J7EZUN37YRK5XNGC","created_at":"2026-07-05T10:19:57.267532+00:00"},{"alias_kind":"pith_short_8","alias_value":"J7EZUN37","created_at":"2026-07-05T10:19:57.267532+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.03654","citing_title":"MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J7EZUN37YRK5XNGCSEJ6GAP5ZQ","json":"https://pith.science/pith/J7EZUN37YRK5XNGCSEJ6GAP5ZQ.json","graph_json":"https://pith.science/api/pith-number/J7EZUN37YRK5XNGCSEJ6GAP5ZQ/graph.json","events_json":"https://pith.science/api/pith-number/J7EZUN37YRK5XNGCSEJ6GAP5ZQ/events.json","paper":"https://pith.science/paper/J7EZUN37"},"agent_actions":{"view_html":"https://pith.science/pith/J7EZUN37YRK5XNGCSEJ6GAP5ZQ","download_json":"https://pith.science/pith/J7EZUN37YRK5XNGCSEJ6GAP5ZQ.json","view_paper":"https://pith.science/paper/J7EZUN37","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.04656&json=true","fetch_graph":"https://pith.science/api/pith-number/J7EZUN37YRK5XNGCSEJ6GAP5ZQ/graph.json","fetch_events":"https://pith.science/api/pith-number/J7EZUN37YRK5XNGCSEJ6GAP5ZQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J7EZUN37YRK5XNGCSEJ6GAP5ZQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J7EZUN37YRK5XNGCSEJ6GAP5ZQ/action/storage_attestation","attest_author":"https://pith.science/pith/J7EZUN37YRK5XNGCSEJ6GAP5ZQ/action/author_attestation","sign_citation":"https://pith.science/pith/J7EZUN37YRK5XNGCSEJ6GAP5ZQ/action/citation_signature","submit_replication":"https://pith.science/pith/J7EZUN37YRK5XNGCSEJ6GAP5ZQ/action/replication_record"}},"created_at":"2026-07-05T10:19:57.267532+00:00","updated_at":"2026-07-05T10:19:57.267532+00:00"}