{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:ZY4X54FONIVCUXECKJVPTR5JGR","short_pith_number":"pith:ZY4X54FO","schema_version":"1.0","canonical_sha256":"ce397ef0ae6a2a2a5c82526af9c7a9347e4c241b23fa8298f6c76320f4c6ce79","source":{"kind":"arxiv","id":"2003.01383","version":2},"attestation_state":"computed","paper":{"title":"Fully Convolutional Networks for Automatically Generating Image Masks to Train Mask R-CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hao Wu, Jan Paul Siebert, Xiangrong Xu","submitted_at":"2020-03-03T08:09:29Z","abstract_excerpt":"This paper proposes a novel automatically generating image masks method for the state-of-the-art Mask R-CNN deep learning method. The Mask R-CNN method achieves the best results in object detection until now, however, it is very time-consuming and laborious to get the object Masks for training, the proposed method is composed by a two-stage design, to automatically generating image masks, the first stage implements a fully convolutional networks (FCN) based segmentation network, the second stage network, a Mask R-CNN based object detection network, which is trained on the object image masks fr"},"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":"2003.01383","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-03T08:09:29Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c1b7c014f971cdfd45efc0ec419507d9e99ceddcaeea5d47e52697e8bbd693b6","abstract_canon_sha256":"67f9f4fa816349a1eea3e0a67d0bd70972602f3be5bc0cf051115fed2e9a842b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:41:46.786213Z","signature_b64":"yfRg5v5tlvd88ej/btrpYzVYTrp0bSty4TiiarXNL725DmeVmt2BbcQLmjLuFz0qyPq+j66r8J5w+62ggAABDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce397ef0ae6a2a2a5c82526af9c7a9347e4c241b23fa8298f6c76320f4c6ce79","last_reissued_at":"2026-07-05T02:41:46.785776Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:41:46.785776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fully Convolutional Networks for Automatically Generating Image Masks to Train Mask R-CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hao Wu, Jan Paul Siebert, Xiangrong Xu","submitted_at":"2020-03-03T08:09:29Z","abstract_excerpt":"This paper proposes a novel automatically generating image masks method for the state-of-the-art Mask R-CNN deep learning method. The Mask R-CNN method achieves the best results in object detection until now, however, it is very time-consuming and laborious to get the object Masks for training, the proposed method is composed by a two-stage design, to automatically generating image masks, the first stage implements a fully convolutional networks (FCN) based segmentation network, the second stage network, a Mask R-CNN based object detection network, which is trained on the object image masks fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.01383","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/2003.01383/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":"2003.01383","created_at":"2026-07-05T02:41:46.785854+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.01383v2","created_at":"2026-07-05T02:41:46.785854+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.01383","created_at":"2026-07-05T02:41:46.785854+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZY4X54FONIVC","created_at":"2026-07-05T02:41:46.785854+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZY4X54FONIVCUXEC","created_at":"2026-07-05T02:41:46.785854+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZY4X54FO","created_at":"2026-07-05T02:41:46.785854+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/ZY4X54FONIVCUXECKJVPTR5JGR","json":"https://pith.science/pith/ZY4X54FONIVCUXECKJVPTR5JGR.json","graph_json":"https://pith.science/api/pith-number/ZY4X54FONIVCUXECKJVPTR5JGR/graph.json","events_json":"https://pith.science/api/pith-number/ZY4X54FONIVCUXECKJVPTR5JGR/events.json","paper":"https://pith.science/paper/ZY4X54FO"},"agent_actions":{"view_html":"https://pith.science/pith/ZY4X54FONIVCUXECKJVPTR5JGR","download_json":"https://pith.science/pith/ZY4X54FONIVCUXECKJVPTR5JGR.json","view_paper":"https://pith.science/paper/ZY4X54FO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.01383&json=true","fetch_graph":"https://pith.science/api/pith-number/ZY4X54FONIVCUXECKJVPTR5JGR/graph.json","fetch_events":"https://pith.science/api/pith-number/ZY4X54FONIVCUXECKJVPTR5JGR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZY4X54FONIVCUXECKJVPTR5JGR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZY4X54FONIVCUXECKJVPTR5JGR/action/storage_attestation","attest_author":"https://pith.science/pith/ZY4X54FONIVCUXECKJVPTR5JGR/action/author_attestation","sign_citation":"https://pith.science/pith/ZY4X54FONIVCUXECKJVPTR5JGR/action/citation_signature","submit_replication":"https://pith.science/pith/ZY4X54FONIVCUXECKJVPTR5JGR/action/replication_record"}},"created_at":"2026-07-05T02:41:46.785854+00:00","updated_at":"2026-07-05T02:41:46.785854+00:00"}