{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2TSHH6GYRFXJV6FUZNXLWXGBIG","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":"9132778b2621f5f487f6423ae57da6aacddd1e8a7f932ea6556857ecd9a566a3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-11T14:38:11Z","title_canon_sha256":"d44d60d9c4f0c05b9605b793ee3205bc94484d020f26e0eb8abd3b2bc3ed76f0"},"schema_version":"1.0","source":{"id":"2308.06160","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.06160","created_at":"2026-07-05T06:58:58Z"},{"alias_kind":"arxiv_version","alias_value":"2308.06160v2","created_at":"2026-07-05T06:58:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.06160","created_at":"2026-07-05T06:58:58Z"},{"alias_kind":"pith_short_12","alias_value":"2TSHH6GYRFXJ","created_at":"2026-07-05T06:58:58Z"},{"alias_kind":"pith_short_16","alias_value":"2TSHH6GYRFXJV6FU","created_at":"2026-07-05T06:58:58Z"},{"alias_kind":"pith_short_8","alias_value":"2TSHH6GY","created_at":"2026-07-05T06:58:58Z"}],"graph_snapshots":[{"event_id":"sha256:61e132ccf6a8df08f4bea7952676b3f9843a6a71fe84b7bfad71f34b15d7a4d5","target":"graph","created_at":"2026-07-05T06:58:58Z","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/2308.06160/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current deep networks are very data-hungry and benefit from training on largescale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can be generated infinitely using generative models such as DALL-E and diffusion models, with minimal effort and cost. In this paper, we present DatasetDM, a generic dataset generation model that can produce diverse synthetic images and the corresponding high-quality perception annotations (e.g., segmentation masks, and depth). Our method builds upon the pre-trained diffusion model and extends text-guided image synthesi","authors_text":"Chunhua Shen, Hao Chen, Hong Zhou, Mike Zheng Shou, Rui Zhao, Weijia Wu, Yefei He, Yuchao Gu, Yuzhong Zhao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-11T14:38:11Z","title":"DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.06160","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:d8d60a5e9987d3b917bd8241ec737f02af41214057ff8a11c3b564ebf602b1e2","target":"record","created_at":"2026-07-05T06:58:58Z","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":"9132778b2621f5f487f6423ae57da6aacddd1e8a7f932ea6556857ecd9a566a3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-11T14:38:11Z","title_canon_sha256":"d44d60d9c4f0c05b9605b793ee3205bc94484d020f26e0eb8abd3b2bc3ed76f0"},"schema_version":"1.0","source":{"id":"2308.06160","kind":"arxiv","version":2}},"canonical_sha256":"d4e473f8d8896e9af8b4cb6ebb5cc141a10975cf1eb26ea3fef211d6d437ba28","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d4e473f8d8896e9af8b4cb6ebb5cc141a10975cf1eb26ea3fef211d6d437ba28","first_computed_at":"2026-07-05T06:58:58.930024Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:58:58.930024Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M2e3oMkWf/RkCq6JvSo1Yhs2mIFYzJzzrAhe3FxCpmgsZeH4IX3TRfE4OjbNNo+gaeJgu/tX7V8mgJVaT/yTDg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:58:58.930441Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.06160","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d8d60a5e9987d3b917bd8241ec737f02af41214057ff8a11c3b564ebf602b1e2","sha256:61e132ccf6a8df08f4bea7952676b3f9843a6a71fe84b7bfad71f34b15d7a4d5"],"state_sha256":"8964798cfec2375d158dca7069ba14f00d4a7783bc3e0328416774ef4b028708"}