{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SOUVAG5H5IYWVCTOM5SCED5AHQ","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":"3c5d7db6a4dd472804ed1339818733cc7f182417142a399d473f57ed0e8ec76c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-25T17:19:26Z","title_canon_sha256":"edcb93e505a0d05d00d92f29005c5fb2add284de604be61d0d71d06ad7ce9048"},"schema_version":"1.0","source":{"id":"2309.14303","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.14303","created_at":"2026-07-05T07:11:59Z"},{"alias_kind":"arxiv_version","alias_value":"2309.14303v4","created_at":"2026-07-05T07:11:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14303","created_at":"2026-07-05T07:11:59Z"},{"alias_kind":"pith_short_12","alias_value":"SOUVAG5H5IYW","created_at":"2026-07-05T07:11:59Z"},{"alias_kind":"pith_short_16","alias_value":"SOUVAG5H5IYWVCTO","created_at":"2026-07-05T07:11:59Z"},{"alias_kind":"pith_short_8","alias_value":"SOUVAG5H","created_at":"2026-07-05T07:11:59Z"}],"graph_snapshots":[{"event_id":"sha256:8dab4b94f7198f9ee54894b7696182d5f4ec0c5e091500da484ff32bbe21c64b","target":"graph","created_at":"2026-07-05T07:11:59Z","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/2309.14303/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Preparing training data for deep vision models is a labor-intensive task. To address this, generative models have emerged as an effective solution for generating synthetic data. While current generative models produce image-level category labels, we propose a novel method for generating pixel-level semantic segmentation labels using the text-to-image generative model Stable Diffusion (SD). By utilizing the text prompts, cross-attention, and self-attention of SD, we introduce three new techniques: class-prompt appending, class-prompt cross-attention, and self-attention exponentiation. These tec","authors_text":"Anh Tran, Khoi Nguyen, Quang Nguyen, Truong Vu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-25T17:19:26Z","title":"Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14303","kind":"arxiv","version":4},"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":"sha256:3fc077fcf999a78f3506de488a949b290621ec0597bf8ebc6e70f4c44819defc","target":"record","created_at":"2026-07-05T07:11:59Z","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":"3c5d7db6a4dd472804ed1339818733cc7f182417142a399d473f57ed0e8ec76c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-25T17:19:26Z","title_canon_sha256":"edcb93e505a0d05d00d92f29005c5fb2add284de604be61d0d71d06ad7ce9048"},"schema_version":"1.0","source":{"id":"2309.14303","kind":"arxiv","version":4}},"canonical_sha256":"93a9501ba7ea316a8a6e6764220fa03c3cf1d12fc53dd66acd7d3ad508572211","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"93a9501ba7ea316a8a6e6764220fa03c3cf1d12fc53dd66acd7d3ad508572211","first_computed_at":"2026-07-05T07:11:59.898661Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:11:59.898661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CjQ1mOVCF10sb5auN+ajz44f2NI90BNHRBZRrz872HfgdYxYA7DLw7cpUhnIgBn07sEKccG+xlF94DGqJslcCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:11:59.899147Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.14303","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3fc077fcf999a78f3506de488a949b290621ec0597bf8ebc6e70f4c44819defc","sha256:8dab4b94f7198f9ee54894b7696182d5f4ec0c5e091500da484ff32bbe21c64b"],"state_sha256":"df42727ea8ab412605ff961526ec1487be568479ed4a856b956532e538c5838d"}