{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NAVDQI3QBRZQU5I65HWEOIPMZ6","short_pith_number":"pith:NAVDQI3Q","schema_version":"1.0","canonical_sha256":"682a3823700c730a751ee9ec4721eccf823472f384cd44212a792e62ce5444d0","source":{"kind":"arxiv","id":"2210.05387","version":1},"attestation_state":"computed","paper":{"title":"Sequential Ensembling for Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Amit Agrawal, Antonio Criminisi, Brandon Smith, Rawal Khirodkar, Siddhartha Chandra","submitted_at":"2022-10-08T22:13:59Z","abstract_excerpt":"Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications. In this work, we explore and benchmark the popular ensembling approach of combining predictions of multiple, independently-trained, state-of-the-art models at test time on popular datasets. Furthermore, we propose a novel method inspired by boosting to sequentially ensemble networks that significantly outperforms the naive ensemble baseline. Our approach trains a cascade of models conditioned on class probabilities pre"},"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":"2210.05387","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-10-08T22:13:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"81b8cf30748e389d23cc4d25f15e38f4992b00e7b63cd41871b4e43760601646","abstract_canon_sha256":"05d1706fc7c5e4a65626b244ab19332845b2c0f9d3c61ea5083b96a46346bf7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:05:16.260948Z","signature_b64":"oxLa1pa7B4sFesLkBmcKpO2UbW6xICQZA1BGQVXeQWbb4wsC8V/gXJGLdyHJl4jxH7XngERQfwf8tZbV47ROBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"682a3823700c730a751ee9ec4721eccf823472f384cd44212a792e62ce5444d0","last_reissued_at":"2026-07-05T05:05:16.260523Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:05:16.260523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sequential Ensembling for Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Amit Agrawal, Antonio Criminisi, Brandon Smith, Rawal Khirodkar, Siddhartha Chandra","submitted_at":"2022-10-08T22:13:59Z","abstract_excerpt":"Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications. In this work, we explore and benchmark the popular ensembling approach of combining predictions of multiple, independently-trained, state-of-the-art models at test time on popular datasets. Furthermore, we propose a novel method inspired by boosting to sequentially ensemble networks that significantly outperforms the naive ensemble baseline. Our approach trains a cascade of models conditioned on class probabilities pre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.05387","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/2210.05387/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":"2210.05387","created_at":"2026-07-05T05:05:16.260579+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.05387v1","created_at":"2026-07-05T05:05:16.260579+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.05387","created_at":"2026-07-05T05:05:16.260579+00:00"},{"alias_kind":"pith_short_12","alias_value":"NAVDQI3QBRZQ","created_at":"2026-07-05T05:05:16.260579+00:00"},{"alias_kind":"pith_short_16","alias_value":"NAVDQI3QBRZQU5I6","created_at":"2026-07-05T05:05:16.260579+00:00"},{"alias_kind":"pith_short_8","alias_value":"NAVDQI3Q","created_at":"2026-07-05T05:05:16.260579+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/NAVDQI3QBRZQU5I65HWEOIPMZ6","json":"https://pith.science/pith/NAVDQI3QBRZQU5I65HWEOIPMZ6.json","graph_json":"https://pith.science/api/pith-number/NAVDQI3QBRZQU5I65HWEOIPMZ6/graph.json","events_json":"https://pith.science/api/pith-number/NAVDQI3QBRZQU5I65HWEOIPMZ6/events.json","paper":"https://pith.science/paper/NAVDQI3Q"},"agent_actions":{"view_html":"https://pith.science/pith/NAVDQI3QBRZQU5I65HWEOIPMZ6","download_json":"https://pith.science/pith/NAVDQI3QBRZQU5I65HWEOIPMZ6.json","view_paper":"https://pith.science/paper/NAVDQI3Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.05387&json=true","fetch_graph":"https://pith.science/api/pith-number/NAVDQI3QBRZQU5I65HWEOIPMZ6/graph.json","fetch_events":"https://pith.science/api/pith-number/NAVDQI3QBRZQU5I65HWEOIPMZ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NAVDQI3QBRZQU5I65HWEOIPMZ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NAVDQI3QBRZQU5I65HWEOIPMZ6/action/storage_attestation","attest_author":"https://pith.science/pith/NAVDQI3QBRZQU5I65HWEOIPMZ6/action/author_attestation","sign_citation":"https://pith.science/pith/NAVDQI3QBRZQU5I65HWEOIPMZ6/action/citation_signature","submit_replication":"https://pith.science/pith/NAVDQI3QBRZQU5I65HWEOIPMZ6/action/replication_record"}},"created_at":"2026-07-05T05:05:16.260579+00:00","updated_at":"2026-07-05T05:05:16.260579+00:00"}