{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:B3EK55QFWO36BGK3MSEXJKXOYB","short_pith_number":"pith:B3EK55QF","schema_version":"1.0","canonical_sha256":"0ec8aef605b3b7e0995b648974aaeec0514235c5a8caeef1214cb93c1dedea39","source":{"kind":"arxiv","id":"2203.08414","version":1},"attestation_state":"computed","paper":{"title":"Unsupervised Semantic Segmentation by Distilling Feature Correspondences","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Bharath Hariharan, Mark Hamilton, Noah Snavely, William T. Freeman, Zhoutong Zhang","submitted_at":"2022-03-16T06:08:47Z","abstract_excerpt":"Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation. To solve this task, algorithms must produce features for every pixel that are both semantically meaningful and compact enough to form distinct clusters. Unlike previous works which achieve this with a single end-to-end framework, we propose to separate feature learning from cluster compactification. Empirically, we show that current unsupervised feature learning frameworks already generate dense features whose correlations are semantically con"},"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":"2203.08414","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-16T06:08:47Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"59ac3cc114c64f1648b2e037ab414fd4bf0ad7aa107b2edf57e1c128e252a1be","abstract_canon_sha256":"07971e7ca78a685bf0a583da77bc7639a3e9e67a074e39e24799863bd8d0bc09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:05:44.347402Z","signature_b64":"Sv4CcXr2vYGyat7Zc1GZSouZEoCKe8uxwnWjM8TUnNZskSA7q9fGC7kmM8SnWh8y9ui7pEiyMkMSpLi7D4g4Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ec8aef605b3b7e0995b648974aaeec0514235c5a8caeef1214cb93c1dedea39","last_reissued_at":"2026-07-05T04:05:44.346976Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:05:44.346976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Semantic Segmentation by Distilling Feature Correspondences","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Bharath Hariharan, Mark Hamilton, Noah Snavely, William T. Freeman, Zhoutong Zhang","submitted_at":"2022-03-16T06:08:47Z","abstract_excerpt":"Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation. To solve this task, algorithms must produce features for every pixel that are both semantically meaningful and compact enough to form distinct clusters. Unlike previous works which achieve this with a single end-to-end framework, we propose to separate feature learning from cluster compactification. Empirically, we show that current unsupervised feature learning frameworks already generate dense features whose correlations are semantically con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.08414","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/2203.08414/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":"2203.08414","created_at":"2026-07-05T04:05:44.347031+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.08414v1","created_at":"2026-07-05T04:05:44.347031+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.08414","created_at":"2026-07-05T04:05:44.347031+00:00"},{"alias_kind":"pith_short_12","alias_value":"B3EK55QFWO36","created_at":"2026-07-05T04:05:44.347031+00:00"},{"alias_kind":"pith_short_16","alias_value":"B3EK55QFWO36BGK3","created_at":"2026-07-05T04:05:44.347031+00:00"},{"alias_kind":"pith_short_8","alias_value":"B3EK55QF","created_at":"2026-07-05T04:05:44.347031+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07169","citing_title":"TACoS: Weakly Supervised Learning of Two-Dimensional Materials from Scribble Annotations to Precise Segmentation","ref_index":28,"is_internal_anchor":true},{"citing_arxiv_id":"2606.22076","citing_title":"Learning Cross-View Semantic Priors for Single-Reference Unseen Object Pose Estimation","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2606.21292","citing_title":"Lightweight 3D Feature Pretraining by Bayesian Inversion of 2D Foundation Models","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29691","citing_title":"Unsupervised Semantic Segmentation Facilitates Model Understanding","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29691","citing_title":"Unsupervised Semantic Segmentation Facilitates Model Understanding","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20737","citing_title":"Resolving Long-Tail Ambiguity in Unsupervised 3D Point Cloud Segmentation with Language Priors","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2605.16147","citing_title":"Registers Matter for Pixel-Space Diffusion Transformers","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2510.16790","citing_title":"Unsupervised Monocular Road Segmentation for Autonomous Driving via Scene Geometry","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11870","citing_title":"Information theoretic underpinning of self-supervised learning by clustering","ref_index":68,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11162","citing_title":"Boxes2Pixels: Learning Defect Segmentation from Noisy SAM Masks","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B3EK55QFWO36BGK3MSEXJKXOYB","json":"https://pith.science/pith/B3EK55QFWO36BGK3MSEXJKXOYB.json","graph_json":"https://pith.science/api/pith-number/B3EK55QFWO36BGK3MSEXJKXOYB/graph.json","events_json":"https://pith.science/api/pith-number/B3EK55QFWO36BGK3MSEXJKXOYB/events.json","paper":"https://pith.science/paper/B3EK55QF"},"agent_actions":{"view_html":"https://pith.science/pith/B3EK55QFWO36BGK3MSEXJKXOYB","download_json":"https://pith.science/pith/B3EK55QFWO36BGK3MSEXJKXOYB.json","view_paper":"https://pith.science/paper/B3EK55QF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.08414&json=true","fetch_graph":"https://pith.science/api/pith-number/B3EK55QFWO36BGK3MSEXJKXOYB/graph.json","fetch_events":"https://pith.science/api/pith-number/B3EK55QFWO36BGK3MSEXJKXOYB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B3EK55QFWO36BGK3MSEXJKXOYB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B3EK55QFWO36BGK3MSEXJKXOYB/action/storage_attestation","attest_author":"https://pith.science/pith/B3EK55QFWO36BGK3MSEXJKXOYB/action/author_attestation","sign_citation":"https://pith.science/pith/B3EK55QFWO36BGK3MSEXJKXOYB/action/citation_signature","submit_replication":"https://pith.science/pith/B3EK55QFWO36BGK3MSEXJKXOYB/action/replication_record"}},"created_at":"2026-07-05T04:05:44.347031+00:00","updated_at":"2026-07-05T04:05:44.347031+00:00"}