{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:VLAVOLFHGNFSIT7CF2HC2C7B3H","short_pith_number":"pith:VLAVOLFH","schema_version":"1.0","canonical_sha256":"aac1572ca7334b244fe22e8e2d0be1d9d6961cc4dcf664f8d30d893ba980367f","source":{"kind":"arxiv","id":"1903.06874","version":1},"attestation_state":"computed","paper":{"title":"Fast Interactive Object Annotation with Curve-GCN","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Amlan Kar, Huan Ling, Jun Gao, Sanja Fidler, Wenzheng Chen","submitted_at":"2019-03-16T03:14:41Z","abstract_excerpt":"Manually labeling objects by tracing their boundaries is a laborious process. In Polygon-RNN++ the authors proposed Polygon-RNN that produces polygonal annotations in a recurrent manner using a CNN-RNN architecture, allowing interactive correction via humans-in-the-loop. We propose a new framework that alleviates the sequential nature of Polygon-RNN, by predicting all vertices simultaneously using a Graph Convolutional Network (GCN). Our model is trained end-to-end. It supports object annotation by either polygons or splines, facilitating labeling efficiency for both line-based and curved obje"},"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":"1903.06874","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-16T03:14:41Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"131367be4f602d7949d958b82fccc514f082567a78458c85754ce536212ee5fa","abstract_canon_sha256":"335de5a09a375bf163cf9806328ad4d8ea1fdd6643929624f9c4c6c8276f4b77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:08.757088Z","signature_b64":"G9X5V8IV0DzfFLSLqNVcVb0yPUkFHwuyF7A0EAfT37hATAZIz2k95aSPGMvtbRqtn0Wr8Si7YoZzQyDEKWeTAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aac1572ca7334b244fe22e8e2d0be1d9d6961cc4dcf664f8d30d893ba980367f","last_reissued_at":"2026-05-17T23:51:08.756567Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:08.756567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Interactive Object Annotation with Curve-GCN","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Amlan Kar, Huan Ling, Jun Gao, Sanja Fidler, Wenzheng Chen","submitted_at":"2019-03-16T03:14:41Z","abstract_excerpt":"Manually labeling objects by tracing their boundaries is a laborious process. In Polygon-RNN++ the authors proposed Polygon-RNN that produces polygonal annotations in a recurrent manner using a CNN-RNN architecture, allowing interactive correction via humans-in-the-loop. We propose a new framework that alleviates the sequential nature of Polygon-RNN, by predicting all vertices simultaneously using a Graph Convolutional Network (GCN). Our model is trained end-to-end. It supports object annotation by either polygons or splines, facilitating labeling efficiency for both line-based and curved obje"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.06874","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":""},"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":"1903.06874","created_at":"2026-05-17T23:51:08.756653+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.06874v1","created_at":"2026-05-17T23:51:08.756653+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.06874","created_at":"2026-05-17T23:51:08.756653+00:00"},{"alias_kind":"pith_short_12","alias_value":"VLAVOLFHGNFS","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"VLAVOLFHGNFSIT7C","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"VLAVOLFH","created_at":"2026-05-18T12:33:30.264802+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/VLAVOLFHGNFSIT7CF2HC2C7B3H","json":"https://pith.science/pith/VLAVOLFHGNFSIT7CF2HC2C7B3H.json","graph_json":"https://pith.science/api/pith-number/VLAVOLFHGNFSIT7CF2HC2C7B3H/graph.json","events_json":"https://pith.science/api/pith-number/VLAVOLFHGNFSIT7CF2HC2C7B3H/events.json","paper":"https://pith.science/paper/VLAVOLFH"},"agent_actions":{"view_html":"https://pith.science/pith/VLAVOLFHGNFSIT7CF2HC2C7B3H","download_json":"https://pith.science/pith/VLAVOLFHGNFSIT7CF2HC2C7B3H.json","view_paper":"https://pith.science/paper/VLAVOLFH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.06874&json=true","fetch_graph":"https://pith.science/api/pith-number/VLAVOLFHGNFSIT7CF2HC2C7B3H/graph.json","fetch_events":"https://pith.science/api/pith-number/VLAVOLFHGNFSIT7CF2HC2C7B3H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VLAVOLFHGNFSIT7CF2HC2C7B3H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VLAVOLFHGNFSIT7CF2HC2C7B3H/action/storage_attestation","attest_author":"https://pith.science/pith/VLAVOLFHGNFSIT7CF2HC2C7B3H/action/author_attestation","sign_citation":"https://pith.science/pith/VLAVOLFHGNFSIT7CF2HC2C7B3H/action/citation_signature","submit_replication":"https://pith.science/pith/VLAVOLFHGNFSIT7CF2HC2C7B3H/action/replication_record"}},"created_at":"2026-05-17T23:51:08.756653+00:00","updated_at":"2026-05-17T23:51:08.756653+00:00"}