{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:XUEYWEWPJL435MRDQ6TOYTXTLG","short_pith_number":"pith:XUEYWEWP","schema_version":"1.0","canonical_sha256":"bd098b12cf4af9beb22387a6ec4ef35999867330e5e71d74d004df5db9e37c67","source":{"kind":"arxiv","id":"1811.10907","version":2},"attestation_state":"computed","paper":{"title":"Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CV","authors_text":"Fan Yang, Ryota Hinami, Shin'ichi Satoh, Steven Ly, Yusuke Matsui","submitted_at":"2018-11-27T10:52:26Z","abstract_excerpt":"Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years. A downside to diffusion is that it performs slowly in comparison to the naive k-NN search, which causes a non-trivial online computational cost on large datasets. To overcome this weakness, we propose a novel diffusion technique in this paper. In our work, instead of applying diffusion to the query, we pre-compute the diffusion results of each element in the database, making the online search a simple linear combination on"},"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":"1811.10907","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T10:52:26Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"53f739c522d6d0f185652ec23581abf4844fd7e1fdb0cd29d43acaea212045cd","abstract_canon_sha256":"cab051003b5a021f02b1bdac3afc548a8c47c7fc75584c86437d3d791de641f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:58.059775Z","signature_b64":"CnyJSnJNPEmrMmExuA/9N2qmpbs0zdtxOKUUHbcdsS3FLAFNBP8dvOoZakARRZ2h2iPhIYpc8804oZ4f11JgAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd098b12cf4af9beb22387a6ec4ef35999867330e5e71d74d004df5db9e37c67","last_reissued_at":"2026-05-17T23:56:58.059053Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:58.059053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CV","authors_text":"Fan Yang, Ryota Hinami, Shin'ichi Satoh, Steven Ly, Yusuke Matsui","submitted_at":"2018-11-27T10:52:26Z","abstract_excerpt":"Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years. A downside to diffusion is that it performs slowly in comparison to the naive k-NN search, which causes a non-trivial online computational cost on large datasets. To overcome this weakness, we propose a novel diffusion technique in this paper. In our work, instead of applying diffusion to the query, we pre-compute the diffusion results of each element in the database, making the online search a simple linear combination on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10907","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":"1811.10907","created_at":"2026-05-17T23:56:58.059174+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.10907v2","created_at":"2026-05-17T23:56:58.059174+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10907","created_at":"2026-05-17T23:56:58.059174+00:00"},{"alias_kind":"pith_short_12","alias_value":"XUEYWEWPJL43","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"XUEYWEWPJL435MRD","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"XUEYWEWP","created_at":"2026-05-18T12:33:01.666342+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/XUEYWEWPJL435MRDQ6TOYTXTLG","json":"https://pith.science/pith/XUEYWEWPJL435MRDQ6TOYTXTLG.json","graph_json":"https://pith.science/api/pith-number/XUEYWEWPJL435MRDQ6TOYTXTLG/graph.json","events_json":"https://pith.science/api/pith-number/XUEYWEWPJL435MRDQ6TOYTXTLG/events.json","paper":"https://pith.science/paper/XUEYWEWP"},"agent_actions":{"view_html":"https://pith.science/pith/XUEYWEWPJL435MRDQ6TOYTXTLG","download_json":"https://pith.science/pith/XUEYWEWPJL435MRDQ6TOYTXTLG.json","view_paper":"https://pith.science/paper/XUEYWEWP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.10907&json=true","fetch_graph":"https://pith.science/api/pith-number/XUEYWEWPJL435MRDQ6TOYTXTLG/graph.json","fetch_events":"https://pith.science/api/pith-number/XUEYWEWPJL435MRDQ6TOYTXTLG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XUEYWEWPJL435MRDQ6TOYTXTLG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XUEYWEWPJL435MRDQ6TOYTXTLG/action/storage_attestation","attest_author":"https://pith.science/pith/XUEYWEWPJL435MRDQ6TOYTXTLG/action/author_attestation","sign_citation":"https://pith.science/pith/XUEYWEWPJL435MRDQ6TOYTXTLG/action/citation_signature","submit_replication":"https://pith.science/pith/XUEYWEWPJL435MRDQ6TOYTXTLG/action/replication_record"}},"created_at":"2026-05-17T23:56:58.059174+00:00","updated_at":"2026-05-17T23:56:58.059174+00:00"}