{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:MD43JATSS3RILNPDOMOVCBKBAT","short_pith_number":"pith:MD43JATS","schema_version":"1.0","canonical_sha256":"60f9b4827296e285b5e3731d51054104d8d4d5ec961320185f6fd3368a78d29b","source":{"kind":"arxiv","id":"1807.09915","version":1},"attestation_state":"computed","paper":{"title":"Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaojian Yu, Peng Zhang, Qi Zheng, Xinge You, Xinyi Zhao","submitted_at":"2018-07-26T01:46:15Z","abstract_excerpt":"Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. In this paper, we present a novel model to address these issues. First, a cross-layer bilinear pooling approach is proposed to capture the inter-layer part feature relations, which r"},"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":"1807.09915","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T01:46:15Z","cross_cats_sorted":[],"title_canon_sha256":"1aab44855f70a03daad6aea48d53c472dd4ec983025d6748c0852ba3bb71d383","abstract_canon_sha256":"147bd5a1f9a88d35c2fbda1f81372a9bfc31a284d178486e53efb8e868c37350"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:46.133688Z","signature_b64":"bcu/BjrNSn43/HNVPTOhGEJ8ltTNd7EKvfpY9Ph5bPdEHDfw4ZvuReAhElWTBMYVxsX+ASbrrspcZLRZp1T9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60f9b4827296e285b5e3731d51054104d8d4d5ec961320185f6fd3368a78d29b","last_reissued_at":"2026-05-18T00:09:46.132966Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:46.132966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaojian Yu, Peng Zhang, Qi Zheng, Xinge You, Xinyi Zhao","submitted_at":"2018-07-26T01:46:15Z","abstract_excerpt":"Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. In this paper, we present a novel model to address these issues. First, a cross-layer bilinear pooling approach is proposed to capture the inter-layer part feature relations, which r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09915","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":""},"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":"1807.09915","created_at":"2026-05-18T00:09:46.133089+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.09915v1","created_at":"2026-05-18T00:09:46.133089+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09915","created_at":"2026-05-18T00:09:46.133089+00:00"},{"alias_kind":"pith_short_12","alias_value":"MD43JATSS3RI","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"MD43JATSS3RILNPD","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"MD43JATS","created_at":"2026-05-18T12:32:37.024351+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/MD43JATSS3RILNPDOMOVCBKBAT","json":"https://pith.science/pith/MD43JATSS3RILNPDOMOVCBKBAT.json","graph_json":"https://pith.science/api/pith-number/MD43JATSS3RILNPDOMOVCBKBAT/graph.json","events_json":"https://pith.science/api/pith-number/MD43JATSS3RILNPDOMOVCBKBAT/events.json","paper":"https://pith.science/paper/MD43JATS"},"agent_actions":{"view_html":"https://pith.science/pith/MD43JATSS3RILNPDOMOVCBKBAT","download_json":"https://pith.science/pith/MD43JATSS3RILNPDOMOVCBKBAT.json","view_paper":"https://pith.science/paper/MD43JATS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.09915&json=true","fetch_graph":"https://pith.science/api/pith-number/MD43JATSS3RILNPDOMOVCBKBAT/graph.json","fetch_events":"https://pith.science/api/pith-number/MD43JATSS3RILNPDOMOVCBKBAT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MD43JATSS3RILNPDOMOVCBKBAT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MD43JATSS3RILNPDOMOVCBKBAT/action/storage_attestation","attest_author":"https://pith.science/pith/MD43JATSS3RILNPDOMOVCBKBAT/action/author_attestation","sign_citation":"https://pith.science/pith/MD43JATSS3RILNPDOMOVCBKBAT/action/citation_signature","submit_replication":"https://pith.science/pith/MD43JATSS3RILNPDOMOVCBKBAT/action/replication_record"}},"created_at":"2026-05-18T00:09:46.133089+00:00","updated_at":"2026-05-18T00:09:46.133089+00:00"}