{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:X7PIRBJCH5QW3YWOIK4DKUOWDA","short_pith_number":"pith:X7PIRBJC","canonical_record":{"source":{"id":"2212.14306","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-29T13:51:54Z","cross_cats_sorted":[],"title_canon_sha256":"aabfc1312b67c072f7b6c20d9b12d3750d712ab79d8c7973a1d072303b397893","abstract_canon_sha256":"cf606d5b70b5a38e1a5efb976bb5a9e7855aebc1b6533150df4d938f3e426242"},"schema_version":"1.0"},"canonical_sha256":"bfde8885223f616de2ce42b83551d6180a3dee2f6c150a3913f770a178f4c47d","source":{"kind":"arxiv","id":"2212.14306","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.14306","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"arxiv_version","alias_value":"2212.14306v2","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.14306","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"pith_short_12","alias_value":"X7PIRBJCH5QW","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"pith_short_16","alias_value":"X7PIRBJCH5QW3YWO","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"pith_short_8","alias_value":"X7PIRBJC","created_at":"2026-07-05T06:47:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:X7PIRBJCH5QW3YWOIK4DKUOWDA","target":"record","payload":{"canonical_record":{"source":{"id":"2212.14306","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-29T13:51:54Z","cross_cats_sorted":[],"title_canon_sha256":"aabfc1312b67c072f7b6c20d9b12d3750d712ab79d8c7973a1d072303b397893","abstract_canon_sha256":"cf606d5b70b5a38e1a5efb976bb5a9e7855aebc1b6533150df4d938f3e426242"},"schema_version":"1.0"},"canonical_sha256":"bfde8885223f616de2ce42b83551d6180a3dee2f6c150a3913f770a178f4c47d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:47:16.814530Z","signature_b64":"FVbXGCf5ng9FK3R0rGAXWWw1PGcr9VuqZdVmvcb3mwX1nEMVExGBk+NoDe4VcKUY+lUikZ3LAnnPJ7er1DyjCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfde8885223f616de2ce42b83551d6180a3dee2f6c150a3913f770a178f4c47d","last_reissued_at":"2026-07-05T06:47:16.814075Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:47:16.814075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.14306","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":"2026-07-05T06:47:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vxuk42lUR/Gm9Yak3Z9egyNRYvOmu0/blL/78V/jrRRQ8ur8SBlClJHObqGowu7Ms5FCFGKYicqJbSljtZwvBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T00:50:20.593238Z"},"content_sha256":"7a7ad8e10dc8afd888cd2e7ea95529052dd96ebf95d9efd5b837c8a2ced2b468","schema_version":"1.0","event_id":"sha256:7a7ad8e10dc8afd888cd2e7ea95529052dd96ebf95d9efd5b837c8a2ced2b468"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:X7PIRBJCH5QW3YWOIK4DKUOWDA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bernhard Kainz, Hadrien Reynaud, Matthew Baugh, Mischa Dombrowski","submitted_at":"2022-12-29T13:51:54Z","abstract_excerpt":"Curating datasets for object segmentation is a difficult task. With the advent of large-scale pre-trained generative models, conditional image generation has been given a significant boost in result quality and ease of use. In this paper, we present a novel method that enables the generation of general foreground-background segmentation models from simple textual descriptions, without requiring segmentation labels. We leverage and explore pre-trained latent diffusion models, to automatically generate weak segmentation masks for concepts and objects. The masks are then used to fine-tune the dif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.14306","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/2212.14306/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":"2026-07-05T06:47:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eUAH55DT+MHKvm/lXzoZQE5Sa9v+C+3QhAEXL3AtDuFPECrGGUuvD+gk4w/o93KuuOQ+SgyPVr1qwTfRGFClAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T00:50:20.594284Z"},"content_sha256":"023053f027fe26512a63025c60e6d215d4727bb5b1b6dd3c167aaf779e6fbe57","schema_version":"1.0","event_id":"sha256:023053f027fe26512a63025c60e6d215d4727bb5b1b6dd3c167aaf779e6fbe57"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X7PIRBJCH5QW3YWOIK4DKUOWDA/bundle.json","state_url":"https://pith.science/pith/X7PIRBJCH5QW3YWOIK4DKUOWDA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X7PIRBJCH5QW3YWOIK4DKUOWDA/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":"2026-08-19T00:50:20Z","links":{"resolver":"https://pith.science/pith/X7PIRBJCH5QW3YWOIK4DKUOWDA","bundle":"https://pith.science/pith/X7PIRBJCH5QW3YWOIK4DKUOWDA/bundle.json","state":"https://pith.science/pith/X7PIRBJCH5QW3YWOIK4DKUOWDA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X7PIRBJCH5QW3YWOIK4DKUOWDA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:X7PIRBJCH5QW3YWOIK4DKUOWDA","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":"cf606d5b70b5a38e1a5efb976bb5a9e7855aebc1b6533150df4d938f3e426242","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-29T13:51:54Z","title_canon_sha256":"aabfc1312b67c072f7b6c20d9b12d3750d712ab79d8c7973a1d072303b397893"},"schema_version":"1.0","source":{"id":"2212.14306","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.14306","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"arxiv_version","alias_value":"2212.14306v2","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.14306","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"pith_short_12","alias_value":"X7PIRBJCH5QW","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"pith_short_16","alias_value":"X7PIRBJCH5QW3YWO","created_at":"2026-07-05T06:47:16Z"},{"alias_kind":"pith_short_8","alias_value":"X7PIRBJC","created_at":"2026-07-05T06:47:16Z"}],"graph_snapshots":[{"event_id":"sha256:023053f027fe26512a63025c60e6d215d4727bb5b1b6dd3c167aaf779e6fbe57","target":"graph","created_at":"2026-07-05T06:47:16Z","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/2212.14306/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Curating datasets for object segmentation is a difficult task. With the advent of large-scale pre-trained generative models, conditional image generation has been given a significant boost in result quality and ease of use. In this paper, we present a novel method that enables the generation of general foreground-background segmentation models from simple textual descriptions, without requiring segmentation labels. We leverage and explore pre-trained latent diffusion models, to automatically generate weak segmentation masks for concepts and objects. The masks are then used to fine-tune the dif","authors_text":"Bernhard Kainz, Hadrien Reynaud, Matthew Baugh, Mischa Dombrowski","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-29T13:51:54Z","title":"Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.14306","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":"sha256:7a7ad8e10dc8afd888cd2e7ea95529052dd96ebf95d9efd5b837c8a2ced2b468","target":"record","created_at":"2026-07-05T06:47:16Z","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":"cf606d5b70b5a38e1a5efb976bb5a9e7855aebc1b6533150df4d938f3e426242","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-12-29T13:51:54Z","title_canon_sha256":"aabfc1312b67c072f7b6c20d9b12d3750d712ab79d8c7973a1d072303b397893"},"schema_version":"1.0","source":{"id":"2212.14306","kind":"arxiv","version":2}},"canonical_sha256":"bfde8885223f616de2ce42b83551d6180a3dee2f6c150a3913f770a178f4c47d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bfde8885223f616de2ce42b83551d6180a3dee2f6c150a3913f770a178f4c47d","first_computed_at":"2026-07-05T06:47:16.814075Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:47:16.814075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FVbXGCf5ng9FK3R0rGAXWWw1PGcr9VuqZdVmvcb3mwX1nEMVExGBk+NoDe4VcKUY+lUikZ3LAnnPJ7er1DyjCA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:47:16.814530Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.14306","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7a7ad8e10dc8afd888cd2e7ea95529052dd96ebf95d9efd5b837c8a2ced2b468","sha256:023053f027fe26512a63025c60e6d215d4727bb5b1b6dd3c167aaf779e6fbe57"],"state_sha256":"029f12b973cfbc80586e3cbf9bdb818fefabafb429af9d3e2fec7f3bffbf43a5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iH2NjOWUaQLunWhTJBFUIiCMWLVa1HOeuU0LTl445y+3hDKbnmRT61nvyMxk1cZGN6MPwohTBwmltrhwIzEMDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T00:50:20.601712Z","bundle_sha256":"340d5382bf39fd2f224ec2c10957faf571a467cdccd47cf80274fe4abcfa637a"}}