{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:R4OWY33QH4EVSGJNM5G6RFHHPN","short_pith_number":"pith:R4OWY33Q","schema_version":"1.0","canonical_sha256":"8f1d6c6f703f0959192d674de894e77b6470c8e1f7bfa48feb2f6ca3200ab2de","source":{"kind":"arxiv","id":"1709.08295","version":1},"attestation_state":"computed","paper":{"title":"Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junjie Zhao, Xiangteng He, Yuxin Peng","submitted_at":"2017-09-25T02:43:49Z","abstract_excerpt":"Discriminative localization is essential for fine-grained image classification task, which devotes to recognizing hundreds of subcategories in the same basic-level category. Reflecting on discriminative regions of objects, key differences among different subcategories are subtle and local. Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers. However, these methods generally have two limitations: (1) Separation of the two-stage learn"},"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":"1709.08295","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-25T02:43:49Z","cross_cats_sorted":[],"title_canon_sha256":"7947928206356bec63963a0cb69d2169f977735508c147670ff9867d2271d842","abstract_canon_sha256":"7015b215051863d0e32ba243283a01d6af21666ad0ccc4975f57de6bcfbd79ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:12.978264Z","signature_b64":"ufEQIp7eoS2Tcf499SMXi3Zb8qPZxFofYt/CL2by35c4lNphiefiKLDD3LKaiKSW4kopNe5fn4i4mzOIZz3MCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f1d6c6f703f0959192d674de894e77b6470c8e1f7bfa48feb2f6ca3200ab2de","last_reissued_at":"2026-05-18T00:29:12.977625Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:12.977625Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junjie Zhao, Xiangteng He, Yuxin Peng","submitted_at":"2017-09-25T02:43:49Z","abstract_excerpt":"Discriminative localization is essential for fine-grained image classification task, which devotes to recognizing hundreds of subcategories in the same basic-level category. Reflecting on discriminative regions of objects, key differences among different subcategories are subtle and local. Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers. However, these methods generally have two limitations: (1) Separation of the two-stage learn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.08295","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":"1709.08295","created_at":"2026-05-18T00:29:12.977736+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.08295v1","created_at":"2026-05-18T00:29:12.977736+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.08295","created_at":"2026-05-18T00:29:12.977736+00:00"},{"alias_kind":"pith_short_12","alias_value":"R4OWY33QH4EV","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"R4OWY33QH4EVSGJN","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"R4OWY33Q","created_at":"2026-05-18T12:31:39.905425+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/R4OWY33QH4EVSGJNM5G6RFHHPN","json":"https://pith.science/pith/R4OWY33QH4EVSGJNM5G6RFHHPN.json","graph_json":"https://pith.science/api/pith-number/R4OWY33QH4EVSGJNM5G6RFHHPN/graph.json","events_json":"https://pith.science/api/pith-number/R4OWY33QH4EVSGJNM5G6RFHHPN/events.json","paper":"https://pith.science/paper/R4OWY33Q"},"agent_actions":{"view_html":"https://pith.science/pith/R4OWY33QH4EVSGJNM5G6RFHHPN","download_json":"https://pith.science/pith/R4OWY33QH4EVSGJNM5G6RFHHPN.json","view_paper":"https://pith.science/paper/R4OWY33Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.08295&json=true","fetch_graph":"https://pith.science/api/pith-number/R4OWY33QH4EVSGJNM5G6RFHHPN/graph.json","fetch_events":"https://pith.science/api/pith-number/R4OWY33QH4EVSGJNM5G6RFHHPN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R4OWY33QH4EVSGJNM5G6RFHHPN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R4OWY33QH4EVSGJNM5G6RFHHPN/action/storage_attestation","attest_author":"https://pith.science/pith/R4OWY33QH4EVSGJNM5G6RFHHPN/action/author_attestation","sign_citation":"https://pith.science/pith/R4OWY33QH4EVSGJNM5G6RFHHPN/action/citation_signature","submit_replication":"https://pith.science/pith/R4OWY33QH4EVSGJNM5G6RFHHPN/action/replication_record"}},"created_at":"2026-05-18T00:29:12.977736+00:00","updated_at":"2026-05-18T00:29:12.977736+00:00"}