{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MEWNZHLIYNISEYCAJKWWXEPOZF","short_pith_number":"pith:MEWNZHLI","schema_version":"1.0","canonical_sha256":"612cdc9d68c3512260404aad6b91eec9623d0d8968950791ef756c4dd9d00035","source":{"kind":"arxiv","id":"2402.16674","version":1},"attestation_state":"computed","paper":{"title":"ConSept: Continual Semantic Segmentation via Adapter-based Vision Transformer","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Dong, Guanglei Yang, Lei Zhang, Wangmeng Zuo","submitted_at":"2024-02-26T15:51:45Z","abstract_excerpt":"In this paper, we delve into the realm of vision transformers for continual semantic segmentation, a problem that has not been sufficiently explored in previous literature. Empirical investigations on the adaptation of existing frameworks to vanilla ViT reveal that incorporating visual adapters into ViTs or fine-tuning ViTs with distillation terms is advantageous for enhancing the segmentation capability of novel classes. These findings motivate us to propose Continual semantic Segmentation via Adapter-based ViT, namely ConSept. Within the simplified architecture of ViT with linear segmentatio"},"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":"2402.16674","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-26T15:51:45Z","cross_cats_sorted":[],"title_canon_sha256":"6cc32d5f0c9e9d6b2e3458d3aa187d8017c2a1ab2e87b5033261d173beaf0ccc","abstract_canon_sha256":"b48419ffa114334c38bd2011520b01a09e6419952c96c9c889575b08467acbbd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:49:24.904724Z","signature_b64":"D1xOLqZg3JMnTtxchwd4Kvrg1XiReu86EghuibJy6gQJw7QFlcaS4q7Us5P+SLeVAQHC+bneqUrIxUun0GNWDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"612cdc9d68c3512260404aad6b91eec9623d0d8968950791ef756c4dd9d00035","last_reissued_at":"2026-07-05T07:49:24.904187Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:49:24.904187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ConSept: Continual Semantic Segmentation via Adapter-based Vision Transformer","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Dong, Guanglei Yang, Lei Zhang, Wangmeng Zuo","submitted_at":"2024-02-26T15:51:45Z","abstract_excerpt":"In this paper, we delve into the realm of vision transformers for continual semantic segmentation, a problem that has not been sufficiently explored in previous literature. Empirical investigations on the adaptation of existing frameworks to vanilla ViT reveal that incorporating visual adapters into ViTs or fine-tuning ViTs with distillation terms is advantageous for enhancing the segmentation capability of novel classes. These findings motivate us to propose Continual semantic Segmentation via Adapter-based ViT, namely ConSept. Within the simplified architecture of ViT with linear segmentatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16674","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/2402.16674/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":"2402.16674","created_at":"2026-07-05T07:49:24.904243+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.16674v1","created_at":"2026-07-05T07:49:24.904243+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16674","created_at":"2026-07-05T07:49:24.904243+00:00"},{"alias_kind":"pith_short_12","alias_value":"MEWNZHLIYNIS","created_at":"2026-07-05T07:49:24.904243+00:00"},{"alias_kind":"pith_short_16","alias_value":"MEWNZHLIYNISEYCA","created_at":"2026-07-05T07:49:24.904243+00:00"},{"alias_kind":"pith_short_8","alias_value":"MEWNZHLI","created_at":"2026-07-05T07:49:24.904243+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.15979","citing_title":"MR-GDINO: Efficient Open-World Continual Object Detection","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MEWNZHLIYNISEYCAJKWWXEPOZF","json":"https://pith.science/pith/MEWNZHLIYNISEYCAJKWWXEPOZF.json","graph_json":"https://pith.science/api/pith-number/MEWNZHLIYNISEYCAJKWWXEPOZF/graph.json","events_json":"https://pith.science/api/pith-number/MEWNZHLIYNISEYCAJKWWXEPOZF/events.json","paper":"https://pith.science/paper/MEWNZHLI"},"agent_actions":{"view_html":"https://pith.science/pith/MEWNZHLIYNISEYCAJKWWXEPOZF","download_json":"https://pith.science/pith/MEWNZHLIYNISEYCAJKWWXEPOZF.json","view_paper":"https://pith.science/paper/MEWNZHLI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.16674&json=true","fetch_graph":"https://pith.science/api/pith-number/MEWNZHLIYNISEYCAJKWWXEPOZF/graph.json","fetch_events":"https://pith.science/api/pith-number/MEWNZHLIYNISEYCAJKWWXEPOZF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MEWNZHLIYNISEYCAJKWWXEPOZF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MEWNZHLIYNISEYCAJKWWXEPOZF/action/storage_attestation","attest_author":"https://pith.science/pith/MEWNZHLIYNISEYCAJKWWXEPOZF/action/author_attestation","sign_citation":"https://pith.science/pith/MEWNZHLIYNISEYCAJKWWXEPOZF/action/citation_signature","submit_replication":"https://pith.science/pith/MEWNZHLIYNISEYCAJKWWXEPOZF/action/replication_record"}},"created_at":"2026-07-05T07:49:24.904243+00:00","updated_at":"2026-07-05T07:49:24.904243+00:00"}