{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZLSVKGMZOC6OGUYEDZP2FWDG4O","short_pith_number":"pith:ZLSVKGMZ","schema_version":"1.0","canonical_sha256":"cae555199970bce353041e5fa2d866e3be55f024d556b76acac97e4edd065a10","source":{"kind":"arxiv","id":"1807.02001","version":2},"attestation_state":"computed","paper":{"title":"Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bertram Drost, Patrick Follmann, Tobias B\\\"ottger","submitted_at":"2018-07-05T13:38:55Z","abstract_excerpt":"Grocery stores have thousands of products that are usually identified using barcodes with a human in the loop. For automated checkout systems, it is necessary to count and classify the groceries efficiently and robustly. One possibility is to use a deep learning algorithm for instance-aware semantic segmentation. Such methods achieve high accuracies but require a large amount of annotated training data.\n  We propose a system to generate the training annotations in a weakly supervised manner, drastically reducing the labeling effort. We assume that for each training image, only the object class"},"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":"1807.02001","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-05T13:38:55Z","cross_cats_sorted":[],"title_canon_sha256":"5fe008bfcad1b52c5eca7fbfce3310a5d297ab2abf1e311fffc556bee2460d56","abstract_canon_sha256":"493ef7edb0b77881b83d3e758485cfc504ca5eb49b2e76f12b067652917f20e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:23.063127Z","signature_b64":"8ZSAyWZhEGOl4Y7wvpkTdUwQ8U/0LnvFY7LOguPk2Eqb67M3gyvNNTpO17JTKL6L3q2J3Yw+HljI0QlxWwiGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cae555199970bce353041e5fa2d866e3be55f024d556b76acac97e4edd065a10","last_reissued_at":"2026-05-18T00:11:23.062593Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:23.062593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bertram Drost, Patrick Follmann, Tobias B\\\"ottger","submitted_at":"2018-07-05T13:38:55Z","abstract_excerpt":"Grocery stores have thousands of products that are usually identified using barcodes with a human in the loop. For automated checkout systems, it is necessary to count and classify the groceries efficiently and robustly. One possibility is to use a deep learning algorithm for instance-aware semantic segmentation. Such methods achieve high accuracies but require a large amount of annotated training data.\n  We propose a system to generate the training annotations in a weakly supervised manner, drastically reducing the labeling effort. We assume that for each training image, only the object class"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02001","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":""},"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":"1807.02001","created_at":"2026-05-18T00:11:23.062665+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.02001v2","created_at":"2026-05-18T00:11:23.062665+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02001","created_at":"2026-05-18T00:11:23.062665+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZLSVKGMZOC6O","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZLSVKGMZOC6OGUYE","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZLSVKGMZ","created_at":"2026-05-18T12:33:07.085635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZLSVKGMZOC6OGUYEDZP2FWDG4O","json":"https://pith.science/pith/ZLSVKGMZOC6OGUYEDZP2FWDG4O.json","graph_json":"https://pith.science/api/pith-number/ZLSVKGMZOC6OGUYEDZP2FWDG4O/graph.json","events_json":"https://pith.science/api/pith-number/ZLSVKGMZOC6OGUYEDZP2FWDG4O/events.json","paper":"https://pith.science/paper/ZLSVKGMZ"},"agent_actions":{"view_html":"https://pith.science/pith/ZLSVKGMZOC6OGUYEDZP2FWDG4O","download_json":"https://pith.science/pith/ZLSVKGMZOC6OGUYEDZP2FWDG4O.json","view_paper":"https://pith.science/paper/ZLSVKGMZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.02001&json=true","fetch_graph":"https://pith.science/api/pith-number/ZLSVKGMZOC6OGUYEDZP2FWDG4O/graph.json","fetch_events":"https://pith.science/api/pith-number/ZLSVKGMZOC6OGUYEDZP2FWDG4O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZLSVKGMZOC6OGUYEDZP2FWDG4O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZLSVKGMZOC6OGUYEDZP2FWDG4O/action/storage_attestation","attest_author":"https://pith.science/pith/ZLSVKGMZOC6OGUYEDZP2FWDG4O/action/author_attestation","sign_citation":"https://pith.science/pith/ZLSVKGMZOC6OGUYEDZP2FWDG4O/action/citation_signature","submit_replication":"https://pith.science/pith/ZLSVKGMZOC6OGUYEDZP2FWDG4O/action/replication_record"}},"created_at":"2026-05-18T00:11:23.062665+00:00","updated_at":"2026-05-18T00:11:23.062665+00:00"}