{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:2E3DMHJ6TTOCKU7ALQJWEPSFBU","short_pith_number":"pith:2E3DMHJ6","schema_version":"1.0","canonical_sha256":"d136361d3e9cdc2553e05c13623e450d2725443f3ccba1dca0923ac0d5dc388f","source":{"kind":"arxiv","id":"2012.07177","version":2},"attestation_state":"computed","paper":{"title":"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aravind Srinivas, Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Quoc V. Le, Rui Qian, Tsung-Yi Lin, Yin Cui","submitted_at":"2020-12-13T22:59:45Z","abstract_excerpt":"Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation ([13, 12]) for instance segmentation where we randomly paste objects onto an image. Prior studies on Copy-Paste relied on modeling the surrounding visual context for pasting the objects. However, we find that the simple mechanism of pasting objects randomly is good enough and can provide solid ga"},"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":"2012.07177","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-12-13T22:59:45Z","cross_cats_sorted":[],"title_canon_sha256":"9457291453f1250e5eab23e090e5df8b238b64e0807f54703c3a6c035aadc9b1","abstract_canon_sha256":"84e67b9887938033baaf23689d17f10aa32087d856644ec4f35ccd116e23484c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:51:37.904428Z","signature_b64":"OBOHI6jfM6uh4vaOs1YTrMrVr+liSxOwsLjWnrkNGue7TsAR0dm/YfWRcDczo8gjMRycKDWArLKxhgamnv7VBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d136361d3e9cdc2553e05c13623e450d2725443f3ccba1dca0923ac0d5dc388f","last_reissued_at":"2026-07-05T02:51:37.903993Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:51:37.903993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aravind Srinivas, Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Quoc V. Le, Rui Qian, Tsung-Yi Lin, Yin Cui","submitted_at":"2020-12-13T22:59:45Z","abstract_excerpt":"Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation ([13, 12]) for instance segmentation where we randomly paste objects onto an image. Prior studies on Copy-Paste relied on modeling the surrounding visual context for pasting the objects. However, we find that the simple mechanism of pasting objects randomly is good enough and can provide solid ga"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.07177","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/2012.07177/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":"2012.07177","created_at":"2026-07-05T02:51:37.904056+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.07177v2","created_at":"2026-07-05T02:51:37.904056+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.07177","created_at":"2026-07-05T02:51:37.904056+00:00"},{"alias_kind":"pith_short_12","alias_value":"2E3DMHJ6TTOC","created_at":"2026-07-05T02:51:37.904056+00:00"},{"alias_kind":"pith_short_16","alias_value":"2E3DMHJ6TTOCKU7A","created_at":"2026-07-05T02:51:37.904056+00:00"},{"alias_kind":"pith_short_8","alias_value":"2E3DMHJ6","created_at":"2026-07-05T02:51:37.904056+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2103.14030","citing_title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2E3DMHJ6TTOCKU7ALQJWEPSFBU","json":"https://pith.science/pith/2E3DMHJ6TTOCKU7ALQJWEPSFBU.json","graph_json":"https://pith.science/api/pith-number/2E3DMHJ6TTOCKU7ALQJWEPSFBU/graph.json","events_json":"https://pith.science/api/pith-number/2E3DMHJ6TTOCKU7ALQJWEPSFBU/events.json","paper":"https://pith.science/paper/2E3DMHJ6"},"agent_actions":{"view_html":"https://pith.science/pith/2E3DMHJ6TTOCKU7ALQJWEPSFBU","download_json":"https://pith.science/pith/2E3DMHJ6TTOCKU7ALQJWEPSFBU.json","view_paper":"https://pith.science/paper/2E3DMHJ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.07177&json=true","fetch_graph":"https://pith.science/api/pith-number/2E3DMHJ6TTOCKU7ALQJWEPSFBU/graph.json","fetch_events":"https://pith.science/api/pith-number/2E3DMHJ6TTOCKU7ALQJWEPSFBU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2E3DMHJ6TTOCKU7ALQJWEPSFBU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2E3DMHJ6TTOCKU7ALQJWEPSFBU/action/storage_attestation","attest_author":"https://pith.science/pith/2E3DMHJ6TTOCKU7ALQJWEPSFBU/action/author_attestation","sign_citation":"https://pith.science/pith/2E3DMHJ6TTOCKU7ALQJWEPSFBU/action/citation_signature","submit_replication":"https://pith.science/pith/2E3DMHJ6TTOCKU7ALQJWEPSFBU/action/replication_record"}},"created_at":"2026-07-05T02:51:37.904056+00:00","updated_at":"2026-07-05T02:51:37.904056+00:00"}