{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:WPENSN6JHIVVSV77CSIJYLWFKO","short_pith_number":"pith:WPENSN6J","canonical_record":{"source":{"id":"2309.12378","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-21T11:47:01Z","cross_cats_sorted":[],"title_canon_sha256":"cc675c3e20370b1e727a4fd7aa6358298575f33dc400332c95a8eb04bc6afd25","abstract_canon_sha256":"911ce16f4d0803ab3c3afcb7f4225a985dcda4bdee82a3a677c8869f2ef2015f"},"schema_version":"1.0"},"canonical_sha256":"b3c8d937c93a2b5957ff14909c2ec5538661648da630b341f5ff777dc7de7cdd","source":{"kind":"arxiv","id":"2309.12378","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.12378","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"arxiv_version","alias_value":"2309.12378v2","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.12378","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"pith_short_12","alias_value":"WPENSN6JHIVV","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"pith_short_16","alias_value":"WPENSN6JHIVVSV77","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"pith_short_8","alias_value":"WPENSN6J","created_at":"2026-07-05T08:00:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:WPENSN6JHIVVSV77CSIJYLWFKO","target":"record","payload":{"canonical_record":{"source":{"id":"2309.12378","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-21T11:47:01Z","cross_cats_sorted":[],"title_canon_sha256":"cc675c3e20370b1e727a4fd7aa6358298575f33dc400332c95a8eb04bc6afd25","abstract_canon_sha256":"911ce16f4d0803ab3c3afcb7f4225a985dcda4bdee82a3a677c8869f2ef2015f"},"schema_version":"1.0"},"canonical_sha256":"b3c8d937c93a2b5957ff14909c2ec5538661648da630b341f5ff777dc7de7cdd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:00:36.137752Z","signature_b64":"eNjLwsw+XkeXOV+2IW/Cf4BjALd3aooAWqX9yhWugDwwJoKPkZ573J9fdIdx9AiDHarKrQ5K3Am5PEVeRkTqDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3c8d937c93a2b5957ff14909c2ec5538661648da630b341f5ff777dc7de7cdd","last_reissued_at":"2026-07-05T08:00:36.137219Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:00:36.137219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.12378","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":"2026-07-05T08:00:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RQxG3WaI+kLzEKPqHCVHNu16CZ1FNyeeS39JPN0W7dDBq47wRcDFz0pnP/arkL4nkZVWtkGNI39BVWAeupwQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T21:27:21.171048Z"},"content_sha256":"9ee6840ff8b04a4ec849fb5934db8758b3d89fed9220579e23fe80a4c7e8bff3","schema_version":"1.0","event_id":"sha256:9ee6840ff8b04a4ec849fb5934db8758b3d89fed9220579e23fe80a4c7e8bff3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:WPENSN6JHIVVSV77CSIJYLWFKO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dominik Engel, Leon Sick, Pedro Hermosilla, Timo Ropinski","submitted_at":"2023-09-21T11:47:01Z","abstract_excerpt":"Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learning have made significant progress on this issue and towards closing the gap to supervised algorithms. To achieve this, semantic knowledge is distilled by learning to correlate randomly sampled features from images across an entire dataset. In this work, we build upon these advances by incorporating information about the structure of the scene into the training process through the use of depth information. We achieve t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.12378","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/2309.12378/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":"2026-07-05T08:00:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Hjvga8RX6/a6/LBAaPbqWRi/yADoT0QgvxeNTrTbYWjJD/FZzbbXOSFhg8AkvrIaHQMaRcJgWa2dp1M00nGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T21:27:21.171429Z"},"content_sha256":"37ae08c8d5c9bba4b32955d74183ee525ee2ee3aed6f81d60f003e1e89a76e06","schema_version":"1.0","event_id":"sha256:37ae08c8d5c9bba4b32955d74183ee525ee2ee3aed6f81d60f003e1e89a76e06"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WPENSN6JHIVVSV77CSIJYLWFKO/bundle.json","state_url":"https://pith.science/pith/WPENSN6JHIVVSV77CSIJYLWFKO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WPENSN6JHIVVSV77CSIJYLWFKO/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":"2026-08-11T21:27:21Z","links":{"resolver":"https://pith.science/pith/WPENSN6JHIVVSV77CSIJYLWFKO","bundle":"https://pith.science/pith/WPENSN6JHIVVSV77CSIJYLWFKO/bundle.json","state":"https://pith.science/pith/WPENSN6JHIVVSV77CSIJYLWFKO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WPENSN6JHIVVSV77CSIJYLWFKO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WPENSN6JHIVVSV77CSIJYLWFKO","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":"911ce16f4d0803ab3c3afcb7f4225a985dcda4bdee82a3a677c8869f2ef2015f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-21T11:47:01Z","title_canon_sha256":"cc675c3e20370b1e727a4fd7aa6358298575f33dc400332c95a8eb04bc6afd25"},"schema_version":"1.0","source":{"id":"2309.12378","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.12378","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"arxiv_version","alias_value":"2309.12378v2","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.12378","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"pith_short_12","alias_value":"WPENSN6JHIVV","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"pith_short_16","alias_value":"WPENSN6JHIVVSV77","created_at":"2026-07-05T08:00:36Z"},{"alias_kind":"pith_short_8","alias_value":"WPENSN6J","created_at":"2026-07-05T08:00:36Z"}],"graph_snapshots":[{"event_id":"sha256:37ae08c8d5c9bba4b32955d74183ee525ee2ee3aed6f81d60f003e1e89a76e06","target":"graph","created_at":"2026-07-05T08:00:36Z","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/2309.12378/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learning have made significant progress on this issue and towards closing the gap to supervised algorithms. To achieve this, semantic knowledge is distilled by learning to correlate randomly sampled features from images across an entire dataset. In this work, we build upon these advances by incorporating information about the structure of the scene into the training process through the use of depth information. We achieve t","authors_text":"Dominik Engel, Leon Sick, Pedro Hermosilla, Timo Ropinski","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-21T11:47:01Z","title":"Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.12378","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":"sha256:9ee6840ff8b04a4ec849fb5934db8758b3d89fed9220579e23fe80a4c7e8bff3","target":"record","created_at":"2026-07-05T08:00:36Z","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":"911ce16f4d0803ab3c3afcb7f4225a985dcda4bdee82a3a677c8869f2ef2015f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-21T11:47:01Z","title_canon_sha256":"cc675c3e20370b1e727a4fd7aa6358298575f33dc400332c95a8eb04bc6afd25"},"schema_version":"1.0","source":{"id":"2309.12378","kind":"arxiv","version":2}},"canonical_sha256":"b3c8d937c93a2b5957ff14909c2ec5538661648da630b341f5ff777dc7de7cdd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b3c8d937c93a2b5957ff14909c2ec5538661648da630b341f5ff777dc7de7cdd","first_computed_at":"2026-07-05T08:00:36.137219Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:00:36.137219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eNjLwsw+XkeXOV+2IW/Cf4BjALd3aooAWqX9yhWugDwwJoKPkZ573J9fdIdx9AiDHarKrQ5K3Am5PEVeRkTqDw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:00:36.137752Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.12378","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9ee6840ff8b04a4ec849fb5934db8758b3d89fed9220579e23fe80a4c7e8bff3","sha256:37ae08c8d5c9bba4b32955d74183ee525ee2ee3aed6f81d60f003e1e89a76e06"],"state_sha256":"31c685f421f5d1493f528bfd1f63d2be8c1913cde4ff5dd99888a95e3078af00"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G4/5U0htOaD6LcmFJU0zdbQM0cZbZYNTi0EPl2U7yHfUiilgrxXOoTPkWI0MIIMTGh89G0GJghhM/09wKjZzCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T21:27:21.173835Z","bundle_sha256":"ce85b2bcf22b75114d302cb5aa83a8c9bbbb5b7e4116e46c4b763b5248f44c10"}}