{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7VAGT2ZU7INY74NPJ2SVOCBGOI","short_pith_number":"pith:7VAGT2ZU","schema_version":"1.0","canonical_sha256":"fd4069eb34fa1b8ff1af4ea557082672197ac18c578d608aaa968317ac15a29f","source":{"kind":"arxiv","id":"1904.08959","version":1},"attestation_state":"computed","paper":{"title":"RepGN:Object Detection with Relational Proposal Graph Network","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Risheng Huang, Xingjian Du, Xuan Shi","submitted_at":"2019-04-18T18:07:20Z","abstract_excerpt":"Region based object detectors achieve the state-of-the-art performance, but few consider to model the relation of proposals. In this paper, we explore the idea of modeling the relationships among the proposals for object detection from the graph learning perspective. Specifically, we present relational proposal graph network (RepGN) which is defined on object proposals and the semantic and spatial relation modeled as the edge. By integrating our RepGN module into object detectors, the relation and context constraints will be introduced to the feature extraction of regions and bounding boxes re"},"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":"1904.08959","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-04-18T18:07:20Z","cross_cats_sorted":[],"title_canon_sha256":"486a89baa7f8599e2b30bb4449e0d3140abf1437d7e40ffcec4745c9bcfe7641","abstract_canon_sha256":"6bec97746334db5bf8bccff309909fb20b9007ced50d0d176f04b6ddd5eb6ac7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:08.791118Z","signature_b64":"YLlaUdBuxiux37ockvQJNVqlxUd1P9teRpeQx2npgOwYWx3jraf5T6UJCH5zk7O1xeS49ft7uZVbGkrkopjNCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd4069eb34fa1b8ff1af4ea557082672197ac18c578d608aaa968317ac15a29f","last_reissued_at":"2026-05-17T23:48:08.790508Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:08.790508Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RepGN:Object Detection with Relational Proposal Graph Network","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Risheng Huang, Xingjian Du, Xuan Shi","submitted_at":"2019-04-18T18:07:20Z","abstract_excerpt":"Region based object detectors achieve the state-of-the-art performance, but few consider to model the relation of proposals. In this paper, we explore the idea of modeling the relationships among the proposals for object detection from the graph learning perspective. Specifically, we present relational proposal graph network (RepGN) which is defined on object proposals and the semantic and spatial relation modeled as the edge. By integrating our RepGN module into object detectors, the relation and context constraints will be introduced to the feature extraction of regions and bounding boxes re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.08959","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":"1904.08959","created_at":"2026-05-17T23:48:08.790583+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.08959v1","created_at":"2026-05-17T23:48:08.790583+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.08959","created_at":"2026-05-17T23:48:08.790583+00:00"},{"alias_kind":"pith_short_12","alias_value":"7VAGT2ZU7INY","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"7VAGT2ZU7INY74NP","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"7VAGT2ZU","created_at":"2026-05-18T12:33:12.712433+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/7VAGT2ZU7INY74NPJ2SVOCBGOI","json":"https://pith.science/pith/7VAGT2ZU7INY74NPJ2SVOCBGOI.json","graph_json":"https://pith.science/api/pith-number/7VAGT2ZU7INY74NPJ2SVOCBGOI/graph.json","events_json":"https://pith.science/api/pith-number/7VAGT2ZU7INY74NPJ2SVOCBGOI/events.json","paper":"https://pith.science/paper/7VAGT2ZU"},"agent_actions":{"view_html":"https://pith.science/pith/7VAGT2ZU7INY74NPJ2SVOCBGOI","download_json":"https://pith.science/pith/7VAGT2ZU7INY74NPJ2SVOCBGOI.json","view_paper":"https://pith.science/paper/7VAGT2ZU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.08959&json=true","fetch_graph":"https://pith.science/api/pith-number/7VAGT2ZU7INY74NPJ2SVOCBGOI/graph.json","fetch_events":"https://pith.science/api/pith-number/7VAGT2ZU7INY74NPJ2SVOCBGOI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7VAGT2ZU7INY74NPJ2SVOCBGOI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7VAGT2ZU7INY74NPJ2SVOCBGOI/action/storage_attestation","attest_author":"https://pith.science/pith/7VAGT2ZU7INY74NPJ2SVOCBGOI/action/author_attestation","sign_citation":"https://pith.science/pith/7VAGT2ZU7INY74NPJ2SVOCBGOI/action/citation_signature","submit_replication":"https://pith.science/pith/7VAGT2ZU7INY74NPJ2SVOCBGOI/action/replication_record"}},"created_at":"2026-05-17T23:48:08.790583+00:00","updated_at":"2026-05-17T23:48:08.790583+00:00"}