{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:L3EDOIPUCTJPM7F3MRRHFWR7D2","short_pith_number":"pith:L3EDOIPU","schema_version":"1.0","canonical_sha256":"5ec83721f414d2f67cbb646272da3f1eac4b04c7ac043f74375230089b689107","source":{"kind":"arxiv","id":"2312.08916","version":2},"attestation_state":"computed","paper":{"title":"Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaowei Fang, Jingxuan He, Lechao Cheng, Mingli Song, Tingting Mu, Zunlei Feng","submitted_at":"2023-12-14T13:21:52Z","abstract_excerpt":"Compared to conventional semantic segmentation with pixel-level supervision, Weakly Supervised Semantic Segmentation (WSSS) with image-level labels poses the challenge that it always focuses on the most discriminative regions, resulting in a disparity between fully supervised conditions. A typical manifestation is the diminished precision on the object boundaries, leading to a deteriorated accuracy of WSSS. To alleviate this issue, we propose to adaptively partition the image content into deterministic regions (e.g., confident foreground and background) and uncertain regions (e.g., object boun"},"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":"2312.08916","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-14T13:21:52Z","cross_cats_sorted":[],"title_canon_sha256":"f35bb914a7b603aab430b7ffb5b9b8cebece67e6689f497d0232c4d77b83e14f","abstract_canon_sha256":"0998dd6ee74cf8df82082299cb17fb402634bbd7275c221c175a6f5f7efaf4cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:24:58.777074Z","signature_b64":"7uE2+lOo/prBD0wotbnJPOpa/VcfABChend5niz9++gUrebTjEQC8vk0nWkOqobsO9cjMxUK0QMScC096bKtBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ec83721f414d2f67cbb646272da3f1eac4b04c7ac043f74375230089b689107","last_reissued_at":"2026-07-05T07:24:58.776634Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:24:58.776634Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaowei Fang, Jingxuan He, Lechao Cheng, Mingli Song, Tingting Mu, Zunlei Feng","submitted_at":"2023-12-14T13:21:52Z","abstract_excerpt":"Compared to conventional semantic segmentation with pixel-level supervision, Weakly Supervised Semantic Segmentation (WSSS) with image-level labels poses the challenge that it always focuses on the most discriminative regions, resulting in a disparity between fully supervised conditions. A typical manifestation is the diminished precision on the object boundaries, leading to a deteriorated accuracy of WSSS. To alleviate this issue, we propose to adaptively partition the image content into deterministic regions (e.g., confident foreground and background) and uncertain regions (e.g., object boun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08916","kind":"arxiv","version":2},"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/2312.08916/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":"2312.08916","created_at":"2026-07-05T07:24:58.776686+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.08916v2","created_at":"2026-07-05T07:24:58.776686+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08916","created_at":"2026-07-05T07:24:58.776686+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3EDOIPUCTJP","created_at":"2026-07-05T07:24:58.776686+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3EDOIPUCTJPM7F3","created_at":"2026-07-05T07:24:58.776686+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3EDOIPU","created_at":"2026-07-05T07:24:58.776686+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.05478","citing_title":"GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L3EDOIPUCTJPM7F3MRRHFWR7D2","json":"https://pith.science/pith/L3EDOIPUCTJPM7F3MRRHFWR7D2.json","graph_json":"https://pith.science/api/pith-number/L3EDOIPUCTJPM7F3MRRHFWR7D2/graph.json","events_json":"https://pith.science/api/pith-number/L3EDOIPUCTJPM7F3MRRHFWR7D2/events.json","paper":"https://pith.science/paper/L3EDOIPU"},"agent_actions":{"view_html":"https://pith.science/pith/L3EDOIPUCTJPM7F3MRRHFWR7D2","download_json":"https://pith.science/pith/L3EDOIPUCTJPM7F3MRRHFWR7D2.json","view_paper":"https://pith.science/paper/L3EDOIPU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.08916&json=true","fetch_graph":"https://pith.science/api/pith-number/L3EDOIPUCTJPM7F3MRRHFWR7D2/graph.json","fetch_events":"https://pith.science/api/pith-number/L3EDOIPUCTJPM7F3MRRHFWR7D2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3EDOIPUCTJPM7F3MRRHFWR7D2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3EDOIPUCTJPM7F3MRRHFWR7D2/action/storage_attestation","attest_author":"https://pith.science/pith/L3EDOIPUCTJPM7F3MRRHFWR7D2/action/author_attestation","sign_citation":"https://pith.science/pith/L3EDOIPUCTJPM7F3MRRHFWR7D2/action/citation_signature","submit_replication":"https://pith.science/pith/L3EDOIPUCTJPM7F3MRRHFWR7D2/action/replication_record"}},"created_at":"2026-07-05T07:24:58.776686+00:00","updated_at":"2026-07-05T07:24:58.776686+00:00"}