{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:SJMRNWYVESCPRKDQIDQKSO2WZI","short_pith_number":"pith:SJMRNWYV","schema_version":"1.0","canonical_sha256":"925916db152484f8a87040e0a93b56ca057860ca343675282c78bba254323a1e","source":{"kind":"arxiv","id":"1504.05277","version":2},"attestation_state":"computed","paper":{"title":"Deep Spatial Pyramid: The Devil is Once Again in the Details","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin-Bin Gao, Jianxin Wu, Weiyao Lin, Xiu-Shen Wei","submitted_at":"2015-04-21T02:13:44Z","abstract_excerpt":"In this paper we show that by carefully making good choices for various detailed but important factors in a visual recognition framework using deep learning features, one can achieve a simple, efficient, yet highly accurate image classification system. We first list 5 important factors, based on both existing researches and ideas proposed in this paper. These important detailed factors include: 1) $\\ell_2$ matrix normalization is more effective than unnormalized or $\\ell_2$ vector normalization, 2) the proposed natural deep spatial pyramid is very effective, and 3) a very small $K$ in Fisher V"},"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":"1504.05277","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-04-21T02:13:44Z","cross_cats_sorted":[],"title_canon_sha256":"7d690021b71c7b690e35ad96e14d23ac1045756d911f397425994d2be782153a","abstract_canon_sha256":"caf8769965f5196e3e3d494db253ca9273ed2d9b341e5b53ee65e5acc483fca3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:18:03.231037Z","signature_b64":"u+gj6WCKC4IjRNrgrKOqxklTHHd65y+2kPQ9hkBeFAvW5FG9EWj2N0w22Lutk1mc3+YQ/V3VgYBzzP9U6/RKDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"925916db152484f8a87040e0a93b56ca057860ca343675282c78bba254323a1e","last_reissued_at":"2026-05-18T02:18:03.230310Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:18:03.230310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Spatial Pyramid: The Devil is Once Again in the Details","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin-Bin Gao, Jianxin Wu, Weiyao Lin, Xiu-Shen Wei","submitted_at":"2015-04-21T02:13:44Z","abstract_excerpt":"In this paper we show that by carefully making good choices for various detailed but important factors in a visual recognition framework using deep learning features, one can achieve a simple, efficient, yet highly accurate image classification system. We first list 5 important factors, based on both existing researches and ideas proposed in this paper. These important detailed factors include: 1) $\\ell_2$ matrix normalization is more effective than unnormalized or $\\ell_2$ vector normalization, 2) the proposed natural deep spatial pyramid is very effective, and 3) a very small $K$ in Fisher V"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.05277","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":""},"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":"1504.05277","created_at":"2026-05-18T02:18:03.230423+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.05277v2","created_at":"2026-05-18T02:18:03.230423+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.05277","created_at":"2026-05-18T02:18:03.230423+00:00"},{"alias_kind":"pith_short_12","alias_value":"SJMRNWYVESCP","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_16","alias_value":"SJMRNWYVESCPRKDQ","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_8","alias_value":"SJMRNWYV","created_at":"2026-05-18T12:29:42.218222+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/SJMRNWYVESCPRKDQIDQKSO2WZI","json":"https://pith.science/pith/SJMRNWYVESCPRKDQIDQKSO2WZI.json","graph_json":"https://pith.science/api/pith-number/SJMRNWYVESCPRKDQIDQKSO2WZI/graph.json","events_json":"https://pith.science/api/pith-number/SJMRNWYVESCPRKDQIDQKSO2WZI/events.json","paper":"https://pith.science/paper/SJMRNWYV"},"agent_actions":{"view_html":"https://pith.science/pith/SJMRNWYVESCPRKDQIDQKSO2WZI","download_json":"https://pith.science/pith/SJMRNWYVESCPRKDQIDQKSO2WZI.json","view_paper":"https://pith.science/paper/SJMRNWYV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.05277&json=true","fetch_graph":"https://pith.science/api/pith-number/SJMRNWYVESCPRKDQIDQKSO2WZI/graph.json","fetch_events":"https://pith.science/api/pith-number/SJMRNWYVESCPRKDQIDQKSO2WZI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SJMRNWYVESCPRKDQIDQKSO2WZI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SJMRNWYVESCPRKDQIDQKSO2WZI/action/storage_attestation","attest_author":"https://pith.science/pith/SJMRNWYVESCPRKDQIDQKSO2WZI/action/author_attestation","sign_citation":"https://pith.science/pith/SJMRNWYVESCPRKDQIDQKSO2WZI/action/citation_signature","submit_replication":"https://pith.science/pith/SJMRNWYVESCPRKDQIDQKSO2WZI/action/replication_record"}},"created_at":"2026-05-18T02:18:03.230423+00:00","updated_at":"2026-05-18T02:18:03.230423+00:00"}