{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:ATNTKCJZRQ4DRZ367ISQTPALEI","short_pith_number":"pith:ATNTKCJZ","canonical_record":{"source":{"id":"2110.05474","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-10-11T17:59:55Z","cross_cats_sorted":[],"title_canon_sha256":"b47294eff687882c2e8549a036182216fb698a9c01e8fdf4cb5be8ee8bb6b1e7","abstract_canon_sha256":"bd062c11fbdfb4d64c95b37d05ce3355061b963b356dfbec0770d58a2a244131"},"schema_version":"1.0"},"canonical_sha256":"04db3509398c3838e77efa2509bc0b220a0d674cc5aecb878a0e624abea6c771","source":{"kind":"arxiv","id":"2110.05474","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.05474","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"arxiv_version","alias_value":"2110.05474v1","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.05474","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"pith_short_12","alias_value":"ATNTKCJZRQ4D","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"pith_short_16","alias_value":"ATNTKCJZRQ4DRZ36","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"pith_short_8","alias_value":"ATNTKCJZ","created_at":"2026-07-05T03:21:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:ATNTKCJZRQ4DRZ367ISQTPALEI","target":"record","payload":{"canonical_record":{"source":{"id":"2110.05474","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-10-11T17:59:55Z","cross_cats_sorted":[],"title_canon_sha256":"b47294eff687882c2e8549a036182216fb698a9c01e8fdf4cb5be8ee8bb6b1e7","abstract_canon_sha256":"bd062c11fbdfb4d64c95b37d05ce3355061b963b356dfbec0770d58a2a244131"},"schema_version":"1.0"},"canonical_sha256":"04db3509398c3838e77efa2509bc0b220a0d674cc5aecb878a0e624abea6c771","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:21:35.383283Z","signature_b64":"GTDsEyPUixWsoFzbbcETp4/JVCAP857KAgSgkzdy74rOqSOG7yAcAD4SnZITxK/L2tdJ9AprJd03gZIdWzP8CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04db3509398c3838e77efa2509bc0b220a0d674cc5aecb878a0e624abea6c771","last_reissued_at":"2026-07-05T03:21:35.382805Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:21:35.382805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.05474","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:21:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iwTTASyF+QcL257b5m2XCwHhj0Jrm4BFiq8KA5flLmqrgqFI3Ev3FOQ4kdhKp9XrR6pFQ830JJcxyjZaTUF9Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T18:22:47.979831Z"},"content_sha256":"6a3b451f389c132c0cd7f5ee90ac5688b2bf1e11f5af69ff14be96529411df22","schema_version":"1.0","event_id":"sha256:6a3b451f389c132c0cd7f5ee90ac5688b2bf1e11f5af69ff14be96529411df22"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:ATNTKCJZRQ4DRZ367ISQTPALEI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fangyun Wei, Han Hu, Hanzhe Hu, Jinshi Cui, Liwei Wang, Qiwei Ye","submitted_at":"2021-10-11T17:59:55Z","abstract_excerpt":"Due to the limited and even imbalanced data, semi-supervised semantic segmentation tends to have poor performance on some certain categories, e.g., tailed categories in Cityscapes dataset which exhibits a long-tailed label distribution. Existing approaches almost all neglect this problem, and treat categories equally. Some popular approaches such as consistency regularization or pseudo-labeling may even harm the learning of under-performing categories, that the predictions or pseudo labels of these categories could be too inaccurate to guide the learning on the unlabeled data. In this paper, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.05474","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/2110.05474/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":"2026-07-05T03:21:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qcF/qp40rzJ6JJcooOZzvsFqeT1Yl1VJNKWqBOS2j9X/7JKggfsTKsqZlGDYS+UMdLi7oJIopSoTuBdDbPcXBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T18:22:47.980320Z"},"content_sha256":"5bfe4677498f9f0ed3c42a1f72788402f5bf176f2ea11a858f68febc09f40d0d","schema_version":"1.0","event_id":"sha256:5bfe4677498f9f0ed3c42a1f72788402f5bf176f2ea11a858f68febc09f40d0d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ATNTKCJZRQ4DRZ367ISQTPALEI/bundle.json","state_url":"https://pith.science/pith/ATNTKCJZRQ4DRZ367ISQTPALEI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ATNTKCJZRQ4DRZ367ISQTPALEI/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":"2026-08-22T18:22:47Z","links":{"resolver":"https://pith.science/pith/ATNTKCJZRQ4DRZ367ISQTPALEI","bundle":"https://pith.science/pith/ATNTKCJZRQ4DRZ367ISQTPALEI/bundle.json","state":"https://pith.science/pith/ATNTKCJZRQ4DRZ367ISQTPALEI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ATNTKCJZRQ4DRZ367ISQTPALEI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:ATNTKCJZRQ4DRZ367ISQTPALEI","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":"bd062c11fbdfb4d64c95b37d05ce3355061b963b356dfbec0770d58a2a244131","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-10-11T17:59:55Z","title_canon_sha256":"b47294eff687882c2e8549a036182216fb698a9c01e8fdf4cb5be8ee8bb6b1e7"},"schema_version":"1.0","source":{"id":"2110.05474","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.05474","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"arxiv_version","alias_value":"2110.05474v1","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.05474","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"pith_short_12","alias_value":"ATNTKCJZRQ4D","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"pith_short_16","alias_value":"ATNTKCJZRQ4DRZ36","created_at":"2026-07-05T03:21:35Z"},{"alias_kind":"pith_short_8","alias_value":"ATNTKCJZ","created_at":"2026-07-05T03:21:35Z"}],"graph_snapshots":[{"event_id":"sha256:5bfe4677498f9f0ed3c42a1f72788402f5bf176f2ea11a858f68febc09f40d0d","target":"graph","created_at":"2026-07-05T03:21:35Z","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/2110.05474/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Due to the limited and even imbalanced data, semi-supervised semantic segmentation tends to have poor performance on some certain categories, e.g., tailed categories in Cityscapes dataset which exhibits a long-tailed label distribution. Existing approaches almost all neglect this problem, and treat categories equally. Some popular approaches such as consistency regularization or pseudo-labeling may even harm the learning of under-performing categories, that the predictions or pseudo labels of these categories could be too inaccurate to guide the learning on the unlabeled data. In this paper, w","authors_text":"Fangyun Wei, Han Hu, Hanzhe Hu, Jinshi Cui, Liwei Wang, Qiwei Ye","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-10-11T17:59:55Z","title":"Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.05474","kind":"arxiv","version":1},"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":"sha256:6a3b451f389c132c0cd7f5ee90ac5688b2bf1e11f5af69ff14be96529411df22","target":"record","created_at":"2026-07-05T03:21:35Z","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":"bd062c11fbdfb4d64c95b37d05ce3355061b963b356dfbec0770d58a2a244131","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-10-11T17:59:55Z","title_canon_sha256":"b47294eff687882c2e8549a036182216fb698a9c01e8fdf4cb5be8ee8bb6b1e7"},"schema_version":"1.0","source":{"id":"2110.05474","kind":"arxiv","version":1}},"canonical_sha256":"04db3509398c3838e77efa2509bc0b220a0d674cc5aecb878a0e624abea6c771","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"04db3509398c3838e77efa2509bc0b220a0d674cc5aecb878a0e624abea6c771","first_computed_at":"2026-07-05T03:21:35.382805Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:21:35.382805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GTDsEyPUixWsoFzbbcETp4/JVCAP857KAgSgkzdy74rOqSOG7yAcAD4SnZITxK/L2tdJ9AprJd03gZIdWzP8CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:21:35.383283Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.05474","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6a3b451f389c132c0cd7f5ee90ac5688b2bf1e11f5af69ff14be96529411df22","sha256:5bfe4677498f9f0ed3c42a1f72788402f5bf176f2ea11a858f68febc09f40d0d"],"state_sha256":"cebe99fdd3a3b40180ce8f85a1ba55bc6a787a285a8e09c5f4e4b3fbde63710d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2jXkMZ70NmkL38nCETFJ9l+rw/3zzP3Yn+fkS1JjS8a+mpPJ+7YjmcAx4XAfiaNAh4Cr1tk1qH8MFa9R4ehJDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T18:22:47.984745Z","bundle_sha256":"81ded1943caa2c33e69f9acdb8f108fd638795bc3b1a44c3b36c648e062a113a"}}