{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AP6NUUDKA4WHN4JXGZSQ3NZOPJ","short_pith_number":"pith:AP6NUUDK","schema_version":"1.0","canonical_sha256":"03fcda506a072c76f13736650db72e7a6ee2f6c2b01cba6a2264456640c875ac","source":{"kind":"arxiv","id":"2210.02025","version":1},"attestation_state":"computed","paper":{"title":"GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Liang, Jiaxu Miao, Wenguan Wang, Yi Yang","submitted_at":"2022-10-05T05:20:49Z","abstract_excerpt":"Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of p(class|pixel feature). Though straightforward, this de facto paradigm neglects the underlying data distribution p(pixel feature|class), and struggles to identify out-of-distribution data. Going beyond this, we propose GMMSeg, a new family of segmentation models that rely on a dense generative classifier for the joint distribution p(pixel feature,class). For each class, GMMSeg builds Gaussian Mixture Models (GMMs) via Expectation-Maximization (EM), so as to capture class-conditional densities. Meanw"},"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":"2210.02025","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-05T05:20:49Z","cross_cats_sorted":[],"title_canon_sha256":"111efbc979852984a9f9c53fca2e3b5d55e23455540fc7503afee7a09927ab0c","abstract_canon_sha256":"e32d09fab1d01945012a97af8da657cae62ff030415ad4322dbc7b848b23978b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:03:42.458269Z","signature_b64":"DrPx+LRFdp4NgBkQ3aj5A2E/51/oUWQPIIqW58B3XpsOU5PEc4C6V2UhoviLT6MaMWwauOuI/tCjcTcd9cj5AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03fcda506a072c76f13736650db72e7a6ee2f6c2b01cba6a2264456640c875ac","last_reissued_at":"2026-07-05T05:03:42.457785Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:03:42.457785Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Liang, Jiaxu Miao, Wenguan Wang, Yi Yang","submitted_at":"2022-10-05T05:20:49Z","abstract_excerpt":"Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of p(class|pixel feature). Though straightforward, this de facto paradigm neglects the underlying data distribution p(pixel feature|class), and struggles to identify out-of-distribution data. Going beyond this, we propose GMMSeg, a new family of segmentation models that rely on a dense generative classifier for the joint distribution p(pixel feature,class). For each class, GMMSeg builds Gaussian Mixture Models (GMMs) via Expectation-Maximization (EM), so as to capture class-conditional densities. Meanw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02025","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/2210.02025/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":"2210.02025","created_at":"2026-07-05T05:03:42.457843+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.02025v1","created_at":"2026-07-05T05:03:42.457843+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02025","created_at":"2026-07-05T05:03:42.457843+00:00"},{"alias_kind":"pith_short_12","alias_value":"AP6NUUDKA4WH","created_at":"2026-07-05T05:03:42.457843+00:00"},{"alias_kind":"pith_short_16","alias_value":"AP6NUUDKA4WHN4JX","created_at":"2026-07-05T05:03:42.457843+00:00"},{"alias_kind":"pith_short_8","alias_value":"AP6NUUDK","created_at":"2026-07-05T05:03:42.457843+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.13130","citing_title":"A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation","ref_index":53,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AP6NUUDKA4WHN4JXGZSQ3NZOPJ","json":"https://pith.science/pith/AP6NUUDKA4WHN4JXGZSQ3NZOPJ.json","graph_json":"https://pith.science/api/pith-number/AP6NUUDKA4WHN4JXGZSQ3NZOPJ/graph.json","events_json":"https://pith.science/api/pith-number/AP6NUUDKA4WHN4JXGZSQ3NZOPJ/events.json","paper":"https://pith.science/paper/AP6NUUDK"},"agent_actions":{"view_html":"https://pith.science/pith/AP6NUUDKA4WHN4JXGZSQ3NZOPJ","download_json":"https://pith.science/pith/AP6NUUDKA4WHN4JXGZSQ3NZOPJ.json","view_paper":"https://pith.science/paper/AP6NUUDK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.02025&json=true","fetch_graph":"https://pith.science/api/pith-number/AP6NUUDKA4WHN4JXGZSQ3NZOPJ/graph.json","fetch_events":"https://pith.science/api/pith-number/AP6NUUDKA4WHN4JXGZSQ3NZOPJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AP6NUUDKA4WHN4JXGZSQ3NZOPJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AP6NUUDKA4WHN4JXGZSQ3NZOPJ/action/storage_attestation","attest_author":"https://pith.science/pith/AP6NUUDKA4WHN4JXGZSQ3NZOPJ/action/author_attestation","sign_citation":"https://pith.science/pith/AP6NUUDKA4WHN4JXGZSQ3NZOPJ/action/citation_signature","submit_replication":"https://pith.science/pith/AP6NUUDKA4WHN4JXGZSQ3NZOPJ/action/replication_record"}},"created_at":"2026-07-05T05:03:42.457843+00:00","updated_at":"2026-07-05T05:03:42.457843+00:00"}