{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NN4BC436PMDLBX5XGSFR47FSG3","short_pith_number":"pith:NN4BC436","schema_version":"1.0","canonical_sha256":"6b7811737e7b06b0dfb7348b1e7cb236c9a67fec9a5fc66872128f970bd8ae08","source":{"kind":"arxiv","id":"2412.03512","version":1},"attestation_state":"computed","paper":{"title":"Distillation of Diffusion Features for Semantic Correspondence","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bj\\\"orn Ommer, Frank Fundel, Johannes Schusterbauer, Vincent Tao Hu","submitted_at":"2024-12-04T17:55:33Z","abstract_excerpt":"Semantic correspondence, the task of determining relationships between different parts of images, underpins various applications including 3D reconstruction, image-to-image translation, object tracking, and visual place recognition. Recent studies have begun to explore representations learned in large generative image models for semantic correspondence, demonstrating promising results. Building on this progress, current state-of-the-art methods rely on combining multiple large models, resulting in high computational demands and reduced efficiency. In this work, we address this challenge by pro"},"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":"2412.03512","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-04T17:55:33Z","cross_cats_sorted":[],"title_canon_sha256":"183221c82413ca168fa9c79fac6848d8ad6309cb90d22e5914ec81f65e5ee73d","abstract_canon_sha256":"97bd9641eb40e13b39d034dc26b9a6f89c619226429cbb0f4b03175e16bac9cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:30.540625Z","signature_b64":"hrdnXwhkqhFenv64BHzKDC3u9xE0htE1NoVWRKVtUUUJbJ4Kvu+04zU6OmAEIYhZp3p8CxCnqqJaouvyS5PLAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b7811737e7b06b0dfb7348b1e7cb236c9a67fec9a5fc66872128f970bd8ae08","last_reissued_at":"2026-07-05T09:44:30.540191Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:30.540191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distillation of Diffusion Features for Semantic Correspondence","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bj\\\"orn Ommer, Frank Fundel, Johannes Schusterbauer, Vincent Tao Hu","submitted_at":"2024-12-04T17:55:33Z","abstract_excerpt":"Semantic correspondence, the task of determining relationships between different parts of images, underpins various applications including 3D reconstruction, image-to-image translation, object tracking, and visual place recognition. Recent studies have begun to explore representations learned in large generative image models for semantic correspondence, demonstrating promising results. Building on this progress, current state-of-the-art methods rely on combining multiple large models, resulting in high computational demands and reduced efficiency. In this work, we address this challenge by pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.03512","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/2412.03512/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":"2412.03512","created_at":"2026-07-05T09:44:30.540252+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.03512v1","created_at":"2026-07-05T09:44:30.540252+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.03512","created_at":"2026-07-05T09:44:30.540252+00:00"},{"alias_kind":"pith_short_12","alias_value":"NN4BC436PMDL","created_at":"2026-07-05T09:44:30.540252+00:00"},{"alias_kind":"pith_short_16","alias_value":"NN4BC436PMDLBX5X","created_at":"2026-07-05T09:44:30.540252+00:00"},{"alias_kind":"pith_short_8","alias_value":"NN4BC436","created_at":"2026-07-05T09:44:30.540252+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/NN4BC436PMDLBX5XGSFR47FSG3","json":"https://pith.science/pith/NN4BC436PMDLBX5XGSFR47FSG3.json","graph_json":"https://pith.science/api/pith-number/NN4BC436PMDLBX5XGSFR47FSG3/graph.json","events_json":"https://pith.science/api/pith-number/NN4BC436PMDLBX5XGSFR47FSG3/events.json","paper":"https://pith.science/paper/NN4BC436"},"agent_actions":{"view_html":"https://pith.science/pith/NN4BC436PMDLBX5XGSFR47FSG3","download_json":"https://pith.science/pith/NN4BC436PMDLBX5XGSFR47FSG3.json","view_paper":"https://pith.science/paper/NN4BC436","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.03512&json=true","fetch_graph":"https://pith.science/api/pith-number/NN4BC436PMDLBX5XGSFR47FSG3/graph.json","fetch_events":"https://pith.science/api/pith-number/NN4BC436PMDLBX5XGSFR47FSG3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NN4BC436PMDLBX5XGSFR47FSG3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NN4BC436PMDLBX5XGSFR47FSG3/action/storage_attestation","attest_author":"https://pith.science/pith/NN4BC436PMDLBX5XGSFR47FSG3/action/author_attestation","sign_citation":"https://pith.science/pith/NN4BC436PMDLBX5XGSFR47FSG3/action/citation_signature","submit_replication":"https://pith.science/pith/NN4BC436PMDLBX5XGSFR47FSG3/action/replication_record"}},"created_at":"2026-07-05T09:44:30.540252+00:00","updated_at":"2026-07-05T09:44:30.540252+00:00"}