{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:FTYPLHGFY4XB2GJS37O552BNEE","short_pith_number":"pith:FTYPLHGF","canonical_record":{"source":{"id":"2310.13026","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-19T07:16:54Z","cross_cats_sorted":[],"title_canon_sha256":"bc30f5b44721108abd23308d7fc8b78f60c6124d1b03cf261bc378869a7b09ee","abstract_canon_sha256":"f73817347f4d29648e6b62941304d3c58bbb331519de39a78dbd4d1d9d262471"},"schema_version":"1.0"},"canonical_sha256":"2cf0f59cc5c72e1d1932dfdddee82d213ca464f17bbb3471e0061006f74133c0","source":{"kind":"arxiv","id":"2310.13026","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.13026","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"arxiv_version","alias_value":"2310.13026v2","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.13026","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"pith_short_12","alias_value":"FTYPLHGFY4XB","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"pith_short_16","alias_value":"FTYPLHGFY4XB2GJS","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"pith_short_8","alias_value":"FTYPLHGF","created_at":"2026-07-05T09:42:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:FTYPLHGFY4XB2GJS37O552BNEE","target":"record","payload":{"canonical_record":{"source":{"id":"2310.13026","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-19T07:16:54Z","cross_cats_sorted":[],"title_canon_sha256":"bc30f5b44721108abd23308d7fc8b78f60c6124d1b03cf261bc378869a7b09ee","abstract_canon_sha256":"f73817347f4d29648e6b62941304d3c58bbb331519de39a78dbd4d1d9d262471"},"schema_version":"1.0"},"canonical_sha256":"2cf0f59cc5c72e1d1932dfdddee82d213ca464f17bbb3471e0061006f74133c0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:20.457961Z","signature_b64":"l0QqcajaEM/Firty6/fnp0usT/g/tYzoz9NDrCQWrBMAVevV3PLIT0gg3QrnJi/K71gHhOzDEQ2EbTEn93n2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cf0f59cc5c72e1d1932dfdddee82d213ca464f17bbb3471e0061006f74133c0","last_reissued_at":"2026-07-05T09:42:20.457178Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:20.457178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.13026","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:42:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EPJsHa7S77JhfSkPZeDztD0dArjM+Kv0WJMfH0g3gdppib5861uTaTyAQYXPhrlbRIzvQWCBL6xjF5MRsjtZBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:47:48.396928Z"},"content_sha256":"9d1d2a8632980188fd7498646b9c020698dbc06b3f5dd9b5eb44aa232f5b5499","schema_version":"1.0","event_id":"sha256:9d1d2a8632980188fd7498646b9c020698dbc06b3f5dd9b5eb44aa232f5b5499"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:FTYPLHGFY4XB2GJS37O552BNEE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Qianru Sun, Zhaozheng Chen","submitted_at":"2023-10-19T07:16:54Z","abstract_excerpt":"The rapid development of deep learning has driven significant progress in image semantic segmentation - a fundamental task in computer vision. Semantic segmentation algorithms often depend on the availability of pixel-level labels (i.e., masks of objects), which are expensive, time-consuming, and labor-intensive. Weakly-supervised semantic segmentation (WSSS) is an effective solution to avoid such labeling. It utilizes only partial or incomplete annotations and provides a cost-effective alternative to fully-supervised semantic segmentation. In this journal, our focus is on the WSSS with image-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.13026","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/2310.13026/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:42:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z6pRX879xx+QQj8B+sjEML19n8I6Xy31YunZGWD9bAZnANxG0Ofno/zvaqR2EqPIm7CDPmjD2sMhL02DxBlWDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:47:48.397546Z"},"content_sha256":"53a1756b1ca47bff7fca74f20e0f04adc5ac700b3beb0377251ab146df242e8f","schema_version":"1.0","event_id":"sha256:53a1756b1ca47bff7fca74f20e0f04adc5ac700b3beb0377251ab146df242e8f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FTYPLHGFY4XB2GJS37O552BNEE/bundle.json","state_url":"https://pith.science/pith/FTYPLHGFY4XB2GJS37O552BNEE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FTYPLHGFY4XB2GJS37O552BNEE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T08:47:48Z","links":{"resolver":"https://pith.science/pith/FTYPLHGFY4XB2GJS37O552BNEE","bundle":"https://pith.science/pith/FTYPLHGFY4XB2GJS37O552BNEE/bundle.json","state":"https://pith.science/pith/FTYPLHGFY4XB2GJS37O552BNEE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FTYPLHGFY4XB2GJS37O552BNEE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:FTYPLHGFY4XB2GJS37O552BNEE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f73817347f4d29648e6b62941304d3c58bbb331519de39a78dbd4d1d9d262471","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-19T07:16:54Z","title_canon_sha256":"bc30f5b44721108abd23308d7fc8b78f60c6124d1b03cf261bc378869a7b09ee"},"schema_version":"1.0","source":{"id":"2310.13026","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.13026","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"arxiv_version","alias_value":"2310.13026v2","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.13026","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"pith_short_12","alias_value":"FTYPLHGFY4XB","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"pith_short_16","alias_value":"FTYPLHGFY4XB2GJS","created_at":"2026-07-05T09:42:20Z"},{"alias_kind":"pith_short_8","alias_value":"FTYPLHGF","created_at":"2026-07-05T09:42:20Z"}],"graph_snapshots":[{"event_id":"sha256:53a1756b1ca47bff7fca74f20e0f04adc5ac700b3beb0377251ab146df242e8f","target":"graph","created_at":"2026-07-05T09:42:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2310.13026/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid development of deep learning has driven significant progress in image semantic segmentation - a fundamental task in computer vision. Semantic segmentation algorithms often depend on the availability of pixel-level labels (i.e., masks of objects), which are expensive, time-consuming, and labor-intensive. Weakly-supervised semantic segmentation (WSSS) is an effective solution to avoid such labeling. It utilizes only partial or incomplete annotations and provides a cost-effective alternative to fully-supervised semantic segmentation. In this journal, our focus is on the WSSS with image-","authors_text":"Qianru Sun, Zhaozheng Chen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-19T07:16:54Z","title":"Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.13026","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9d1d2a8632980188fd7498646b9c020698dbc06b3f5dd9b5eb44aa232f5b5499","target":"record","created_at":"2026-07-05T09:42:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f73817347f4d29648e6b62941304d3c58bbb331519de39a78dbd4d1d9d262471","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-19T07:16:54Z","title_canon_sha256":"bc30f5b44721108abd23308d7fc8b78f60c6124d1b03cf261bc378869a7b09ee"},"schema_version":"1.0","source":{"id":"2310.13026","kind":"arxiv","version":2}},"canonical_sha256":"2cf0f59cc5c72e1d1932dfdddee82d213ca464f17bbb3471e0061006f74133c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2cf0f59cc5c72e1d1932dfdddee82d213ca464f17bbb3471e0061006f74133c0","first_computed_at":"2026-07-05T09:42:20.457178Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:42:20.457178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"l0QqcajaEM/Firty6/fnp0usT/g/tYzoz9NDrCQWrBMAVevV3PLIT0gg3QrnJi/K71gHhOzDEQ2EbTEn93n2Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:42:20.457961Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.13026","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d1d2a8632980188fd7498646b9c020698dbc06b3f5dd9b5eb44aa232f5b5499","sha256:53a1756b1ca47bff7fca74f20e0f04adc5ac700b3beb0377251ab146df242e8f"],"state_sha256":"d46480f5853a78cc2057eb2da96739693efa0b2bd56bd0ca6fcf24b5033328ee"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D0kT5Jy+bg84w+eih3BRR5RReO9ioBVFOtTj/UwwJDo4w5RxiVVCbbGBFnCc8emz7RLc1jV7zCiubAU/FdeVAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T08:47:48.401731Z","bundle_sha256":"340c5103c24b1a9f6a1386be626c8e1145ada049b624192bfe9ccb13ed75455d"}}