{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:H6NVU6PUZQONSZH5HCBZH5QWBB","short_pith_number":"pith:H6NVU6PU","schema_version":"1.0","canonical_sha256":"3f9b5a79f4cc1cd964fd388393f6160840d68b0b55017ff9a26dba4bea9c111a","source":{"kind":"arxiv","id":"2507.08404","version":1},"attestation_state":"computed","paper":{"title":"Deep Hashing with Semantic Hash Centers for Image Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Dell Zhang, Li Chen, Rui Liu, Xudong Ma, Yong Chen, Yuxiang Zhou","submitted_at":"2025-07-11T08:22:27Z","abstract_excerpt":"Deep hashing is an effective approach for large-scale image retrieval. Current methods are typically classified by their supervision types: point-wise, pair-wise, and list-wise. Recent point-wise techniques (e.g., CSQ, MDS) have improved retrieval performance by pre-assigning a hash center to each class, enhancing the discriminability of hash codes across various datasets. However, these methods rely on data-independent algorithms to generate hash centers, which neglect the semantic relationships between classes and may degrade retrieval performance.\n  This paper introduces the concept of sema"},"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":"2507.08404","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-11T08:22:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c5adeff1df864151b126871008e84e46310e999a759060ff25af53c4141566cc","abstract_canon_sha256":"e3dff4a45207e982b24d1940f86eb12e2037782dcc550f5ee039891b57b6ece2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:37.500590Z","signature_b64":"WQb3uH31saASEY9fxK1EzjDXleD0VdJzPqHKJWpXtTdSQXMZg8+AIZQW6qz2Vh/ZjiQr/+MWnslNLsQ6qacXAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f9b5a79f4cc1cd964fd388393f6160840d68b0b55017ff9a26dba4bea9c111a","last_reissued_at":"2026-07-05T11:35:37.499938Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:37.499938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Hashing with Semantic Hash Centers for Image Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Dell Zhang, Li Chen, Rui Liu, Xudong Ma, Yong Chen, Yuxiang Zhou","submitted_at":"2025-07-11T08:22:27Z","abstract_excerpt":"Deep hashing is an effective approach for large-scale image retrieval. Current methods are typically classified by their supervision types: point-wise, pair-wise, and list-wise. Recent point-wise techniques (e.g., CSQ, MDS) have improved retrieval performance by pre-assigning a hash center to each class, enhancing the discriminability of hash codes across various datasets. However, these methods rely on data-independent algorithms to generate hash centers, which neglect the semantic relationships between classes and may degrade retrieval performance.\n  This paper introduces the concept of sema"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08404","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/2507.08404/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":"2507.08404","created_at":"2026-07-05T11:35:37.500002+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.08404v1","created_at":"2026-07-05T11:35:37.500002+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08404","created_at":"2026-07-05T11:35:37.500002+00:00"},{"alias_kind":"pith_short_12","alias_value":"H6NVU6PUZQON","created_at":"2026-07-05T11:35:37.500002+00:00"},{"alias_kind":"pith_short_16","alias_value":"H6NVU6PUZQONSZH5","created_at":"2026-07-05T11:35:37.500002+00:00"},{"alias_kind":"pith_short_8","alias_value":"H6NVU6PU","created_at":"2026-07-05T11:35:37.500002+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/H6NVU6PUZQONSZH5HCBZH5QWBB","json":"https://pith.science/pith/H6NVU6PUZQONSZH5HCBZH5QWBB.json","graph_json":"https://pith.science/api/pith-number/H6NVU6PUZQONSZH5HCBZH5QWBB/graph.json","events_json":"https://pith.science/api/pith-number/H6NVU6PUZQONSZH5HCBZH5QWBB/events.json","paper":"https://pith.science/paper/H6NVU6PU"},"agent_actions":{"view_html":"https://pith.science/pith/H6NVU6PUZQONSZH5HCBZH5QWBB","download_json":"https://pith.science/pith/H6NVU6PUZQONSZH5HCBZH5QWBB.json","view_paper":"https://pith.science/paper/H6NVU6PU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.08404&json=true","fetch_graph":"https://pith.science/api/pith-number/H6NVU6PUZQONSZH5HCBZH5QWBB/graph.json","fetch_events":"https://pith.science/api/pith-number/H6NVU6PUZQONSZH5HCBZH5QWBB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H6NVU6PUZQONSZH5HCBZH5QWBB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H6NVU6PUZQONSZH5HCBZH5QWBB/action/storage_attestation","attest_author":"https://pith.science/pith/H6NVU6PUZQONSZH5HCBZH5QWBB/action/author_attestation","sign_citation":"https://pith.science/pith/H6NVU6PUZQONSZH5HCBZH5QWBB/action/citation_signature","submit_replication":"https://pith.science/pith/H6NVU6PUZQONSZH5HCBZH5QWBB/action/replication_record"}},"created_at":"2026-07-05T11:35:37.500002+00:00","updated_at":"2026-07-05T11:35:37.500002+00:00"}