{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:WRIRWEJ7ZAGQPNJB3PIKTD4LVW","short_pith_number":"pith:WRIRWEJ7","canonical_record":{"source":{"id":"2304.03284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-06T17:59:57Z","cross_cats_sorted":[],"title_canon_sha256":"786756233c01bcc1ab9e1bf831c622db5d5e5b5fbd620136edacd8854c372c6c","abstract_canon_sha256":"f8829abf3500ccb3c09859beeb1e132405ad422a3f62ecdff1fed989d394ac72"},"schema_version":"1.0"},"canonical_sha256":"b4511b113fc80d07b521dbd0a98f8bada089116d9e6738bcb187428d1a29c8c5","source":{"kind":"arxiv","id":"2304.03284","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.03284","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"arxiv_version","alias_value":"2304.03284v1","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.03284","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"pith_short_12","alias_value":"WRIRWEJ7ZAGQ","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"pith_short_16","alias_value":"WRIRWEJ7ZAGQPNJB","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"pith_short_8","alias_value":"WRIRWEJ7","created_at":"2026-07-05T05:58:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:WRIRWEJ7ZAGQPNJB3PIKTD4LVW","target":"record","payload":{"canonical_record":{"source":{"id":"2304.03284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-06T17:59:57Z","cross_cats_sorted":[],"title_canon_sha256":"786756233c01bcc1ab9e1bf831c622db5d5e5b5fbd620136edacd8854c372c6c","abstract_canon_sha256":"f8829abf3500ccb3c09859beeb1e132405ad422a3f62ecdff1fed989d394ac72"},"schema_version":"1.0"},"canonical_sha256":"b4511b113fc80d07b521dbd0a98f8bada089116d9e6738bcb187428d1a29c8c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:58:47.732776Z","signature_b64":"68V3tOUfd6egj3yp7E9aRn3D5xj7KL1zrbRcz5YExpH6cLxrlxhRmTzVhomtbbjv4hIDa0R9WXDFHaRnC8KLBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4511b113fc80d07b521dbd0a98f8bada089116d9e6738bcb187428d1a29c8c5","last_reissued_at":"2026-07-05T05:58:47.732353Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:58:47.732353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.03284","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":"2026-07-05T05:58:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UOFlX7hVhRHiU0IRMPiIhMYQSfJOAmthMRKiHoCGvBTV7qjEordeBJ3R609WH+OJ29ljGCGnlZq1pfI0dVRLAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T13:52:55.665633Z"},"content_sha256":"0503d152474abded77b1468afb9583999b43dbd401a871c18843ea06854992ec","schema_version":"1.0","event_id":"sha256:0503d152474abded77b1468afb9583999b43dbd401a871c18843ea06854992ec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:WRIRWEJ7ZAGQPNJB3PIKTD4LVW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SegGPT: Segmenting Everything In Context","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunhua Shen, Tiejun Huang, Wen Wang, Xiaosong Zhang, Xinlong Wang, Yue Cao","submitted_at":"2023-04-06T17:59:57Z","abstract_excerpt":"We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-context coloring problem with random color mapping for each data sample. The objective is to accomplish diverse tasks according to the context, rather than relying on specific colors. After training, SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.03284","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/2304.03284/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":"2026-07-05T05:58:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hucwdET4soNTyPLVOmuANnGD46FCvskoi+Yyq5BdtM9ZsUX89BNmThO/h+u+pTEpM7RtBF4JERKm6SUEz4+eBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T13:52:55.666127Z"},"content_sha256":"8318f63beb43b682aa917f5a30b2404efe0c273c78ba547b9ee617783bc532c7","schema_version":"1.0","event_id":"sha256:8318f63beb43b682aa917f5a30b2404efe0c273c78ba547b9ee617783bc532c7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/bundle.json","state_url":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/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":"2026-08-06T13:52:55Z","links":{"resolver":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW","bundle":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/bundle.json","state":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WRIRWEJ7ZAGQPNJB3PIKTD4LVW","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":"f8829abf3500ccb3c09859beeb1e132405ad422a3f62ecdff1fed989d394ac72","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-06T17:59:57Z","title_canon_sha256":"786756233c01bcc1ab9e1bf831c622db5d5e5b5fbd620136edacd8854c372c6c"},"schema_version":"1.0","source":{"id":"2304.03284","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.03284","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"arxiv_version","alias_value":"2304.03284v1","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.03284","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"pith_short_12","alias_value":"WRIRWEJ7ZAGQ","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"pith_short_16","alias_value":"WRIRWEJ7ZAGQPNJB","created_at":"2026-07-05T05:58:47Z"},{"alias_kind":"pith_short_8","alias_value":"WRIRWEJ7","created_at":"2026-07-05T05:58:47Z"}],"graph_snapshots":[{"event_id":"sha256:8318f63beb43b682aa917f5a30b2404efe0c273c78ba547b9ee617783bc532c7","target":"graph","created_at":"2026-07-05T05:58:47Z","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/2304.03284/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-context coloring problem with random color mapping for each data sample. The objective is to accomplish diverse tasks according to the context, rather than relying on specific colors. After training, SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inf","authors_text":"Chunhua Shen, Tiejun Huang, Wen Wang, Xiaosong Zhang, Xinlong Wang, Yue Cao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-06T17:59:57Z","title":"SegGPT: Segmenting Everything In Context"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.03284","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":"sha256:0503d152474abded77b1468afb9583999b43dbd401a871c18843ea06854992ec","target":"record","created_at":"2026-07-05T05:58:47Z","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":"f8829abf3500ccb3c09859beeb1e132405ad422a3f62ecdff1fed989d394ac72","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-06T17:59:57Z","title_canon_sha256":"786756233c01bcc1ab9e1bf831c622db5d5e5b5fbd620136edacd8854c372c6c"},"schema_version":"1.0","source":{"id":"2304.03284","kind":"arxiv","version":1}},"canonical_sha256":"b4511b113fc80d07b521dbd0a98f8bada089116d9e6738bcb187428d1a29c8c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b4511b113fc80d07b521dbd0a98f8bada089116d9e6738bcb187428d1a29c8c5","first_computed_at":"2026-07-05T05:58:47.732353Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:58:47.732353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"68V3tOUfd6egj3yp7E9aRn3D5xj7KL1zrbRcz5YExpH6cLxrlxhRmTzVhomtbbjv4hIDa0R9WXDFHaRnC8KLBw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:58:47.732776Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.03284","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0503d152474abded77b1468afb9583999b43dbd401a871c18843ea06854992ec","sha256:8318f63beb43b682aa917f5a30b2404efe0c273c78ba547b9ee617783bc532c7"],"state_sha256":"b1bf94a0257f5622b37f1d38f58695fed329d66b06fa891e8720d3ecdb30fd5e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fVzRe0Q9YuzcgCxQZZpcP5Ig6AkckvTC7gzIB9RARdJhDTjhwxbOq0EF9ecw/x0sJYxXqlN3vzBhU3qu6uGdAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T13:52:55.669973Z","bundle_sha256":"94f01611b2339b538797c03aa3f9f4ea0cc1d20061ab3b263596bca5bcdf407a"}}