{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IVESAQA7WX7QPE2KWTSVIYIBVQ","short_pith_number":"pith:IVESAQA7","schema_version":"1.0","canonical_sha256":"454920401fb5ff07934ab4e5546101ac3bf39b479e205bc818bf1ae48557c24d","source":{"kind":"arxiv","id":"2509.00527","version":1},"attestation_state":"computed","paper":{"title":"Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jia Li, Ruitao Wu, Yifan Zhao","submitted_at":"2025-08-30T15:18:58Z","abstract_excerpt":"Class-Incremental Semantic Segmentation (CISS) requires continuous learning of newly introduced classes while retaining knowledge of past classes. By abstracting mainstream methods into two stages (visual feature extraction and prototype-feature matching), we identify a more fundamental challenge termed catastrophic semantic entanglement. This phenomenon involves Prototype-Feature Entanglement caused by semantic misalignment during the incremental process, and Background-Increment Entanglement due to dynamic data evolution. Existing techniques, which rely on visual feature learning without suf"},"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":"2509.00527","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-30T15:18:58Z","cross_cats_sorted":[],"title_canon_sha256":"4011a4561dd8c0100063f9bbf10cc97b98bb651c5d6457beae11d1ce1fb6bff0","abstract_canon_sha256":"cb7f33ba2deda36a1a350f3105ea2a73911af2d99d2fe06dc20e563a901619ab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:17.858974Z","signature_b64":"y3UqceQJ7PCkZ1ghf7wtq5HASsZ3jps5ClbTHoGSDbdtMp88zRS2W+gx7If3L5peQlvacLHdsPHvxvYI1RpLCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"454920401fb5ff07934ab4e5546101ac3bf39b479e205bc818bf1ae48557c24d","last_reissued_at":"2026-07-05T12:02:17.858548Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:17.858548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jia Li, Ruitao Wu, Yifan Zhao","submitted_at":"2025-08-30T15:18:58Z","abstract_excerpt":"Class-Incremental Semantic Segmentation (CISS) requires continuous learning of newly introduced classes while retaining knowledge of past classes. By abstracting mainstream methods into two stages (visual feature extraction and prototype-feature matching), we identify a more fundamental challenge termed catastrophic semantic entanglement. This phenomenon involves Prototype-Feature Entanglement caused by semantic misalignment during the incremental process, and Background-Increment Entanglement due to dynamic data evolution. Existing techniques, which rely on visual feature learning without suf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.00527","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/2509.00527/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":"2509.00527","created_at":"2026-07-05T12:02:17.858608+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.00527v1","created_at":"2026-07-05T12:02:17.858608+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.00527","created_at":"2026-07-05T12:02:17.858608+00:00"},{"alias_kind":"pith_short_12","alias_value":"IVESAQA7WX7Q","created_at":"2026-07-05T12:02:17.858608+00:00"},{"alias_kind":"pith_short_16","alias_value":"IVESAQA7WX7QPE2K","created_at":"2026-07-05T12:02:17.858608+00:00"},{"alias_kind":"pith_short_8","alias_value":"IVESAQA7","created_at":"2026-07-05T12:02:17.858608+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/IVESAQA7WX7QPE2KWTSVIYIBVQ","json":"https://pith.science/pith/IVESAQA7WX7QPE2KWTSVIYIBVQ.json","graph_json":"https://pith.science/api/pith-number/IVESAQA7WX7QPE2KWTSVIYIBVQ/graph.json","events_json":"https://pith.science/api/pith-number/IVESAQA7WX7QPE2KWTSVIYIBVQ/events.json","paper":"https://pith.science/paper/IVESAQA7"},"agent_actions":{"view_html":"https://pith.science/pith/IVESAQA7WX7QPE2KWTSVIYIBVQ","download_json":"https://pith.science/pith/IVESAQA7WX7QPE2KWTSVIYIBVQ.json","view_paper":"https://pith.science/paper/IVESAQA7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.00527&json=true","fetch_graph":"https://pith.science/api/pith-number/IVESAQA7WX7QPE2KWTSVIYIBVQ/graph.json","fetch_events":"https://pith.science/api/pith-number/IVESAQA7WX7QPE2KWTSVIYIBVQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IVESAQA7WX7QPE2KWTSVIYIBVQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IVESAQA7WX7QPE2KWTSVIYIBVQ/action/storage_attestation","attest_author":"https://pith.science/pith/IVESAQA7WX7QPE2KWTSVIYIBVQ/action/author_attestation","sign_citation":"https://pith.science/pith/IVESAQA7WX7QPE2KWTSVIYIBVQ/action/citation_signature","submit_replication":"https://pith.science/pith/IVESAQA7WX7QPE2KWTSVIYIBVQ/action/replication_record"}},"created_at":"2026-07-05T12:02:17.858608+00:00","updated_at":"2026-07-05T12:02:17.858608+00:00"}