{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:WRIRWEJ7ZAGQPNJB3PIKTD4LVW","short_pith_number":"pith:WRIRWEJ7","schema_version":"1.0","canonical_sha256":"b4511b113fc80d07b521dbd0a98f8bada089116d9e6738bcb187428d1a29c8c5","source":{"kind":"arxiv","id":"2304.03284","version":1},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"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"},"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"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2304.03284","created_at":"2026-07-05T05:58:47.732413+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.03284v1","created_at":"2026-07-05T05:58:47.732413+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.03284","created_at":"2026-07-05T05:58:47.732413+00:00"},{"alias_kind":"pith_short_12","alias_value":"WRIRWEJ7ZAGQ","created_at":"2026-07-05T05:58:47.732413+00:00"},{"alias_kind":"pith_short_16","alias_value":"WRIRWEJ7ZAGQPNJB","created_at":"2026-07-05T05:58:47.732413+00:00"},{"alias_kind":"pith_short_8","alias_value":"WRIRWEJ7","created_at":"2026-07-05T05:58:47.732413+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08420","citing_title":"CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2501.05281","citing_title":"Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18010","citing_title":"Functionalization via Structure Completion and Motion Rectification","ref_index":166,"is_internal_anchor":false},{"citing_arxiv_id":"2510.13896","citing_title":"GenCellAgent: Generalizable, Training-Free Cellular Image Segmentation via Large Language Model Agents","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2303.16199","citing_title":"LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention","ref_index":289,"is_internal_anchor":false},{"citing_arxiv_id":"2603.27494","citing_title":"Learning to Focus and Precise Cropping: A Reinforcement Learning Framework with Information Gaps and Grounding Loss for MLLMs","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03950","citing_title":"UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05651","citing_title":"Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective","ref_index":70,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW","json":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW.json","graph_json":"https://pith.science/api/pith-number/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/graph.json","events_json":"https://pith.science/api/pith-number/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/events.json","paper":"https://pith.science/paper/WRIRWEJ7"},"agent_actions":{"view_html":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW","download_json":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW.json","view_paper":"https://pith.science/paper/WRIRWEJ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.03284&json=true","fetch_graph":"https://pith.science/api/pith-number/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/graph.json","fetch_events":"https://pith.science/api/pith-number/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/action/storage_attestation","attest_author":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/action/author_attestation","sign_citation":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/action/citation_signature","submit_replication":"https://pith.science/pith/WRIRWEJ7ZAGQPNJB3PIKTD4LVW/action/replication_record"}},"created_at":"2026-07-05T05:58:47.732413+00:00","updated_at":"2026-07-05T05:58:47.732413+00:00"}