{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:UJNVYFWIWEAY7HDEFIV2XNIYBF","short_pith_number":"pith:UJNVYFWI","schema_version":"1.0","canonical_sha256":"a25b5c16c8b1018f9c642a2babb51809440b1d86a9b0e31406841a8fc5cde1db","source":{"kind":"arxiv","id":"1801.08110","version":1},"attestation_state":"computed","paper":{"title":"The challenge of simultaneous object detection and pose estimation: a comparative study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Carolina Redondo-Cabrera, Daniel O\\~noro-Rubio, Pedro Gil-Jim\\'enez, Roberto J. L\\'opez-Sastre","submitted_at":"2018-01-24T18:21:38Z","abstract_excerpt":"Detecting objects and estimating their pose remains as one of the major challenges of the computer vision research community. There exists a compromise between localizing the objects and estimating their viewpoints. The detector ideally needs to be view-invariant, while the pose estimation process should be able to generalize towards the category-level. This work is an exploration of using deep learning models for solving both problems simultaneously. For doing so, we propose three novel deep learning architectures, which are able to perform a joint detection and pose estimation, where we grad"},"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":"1801.08110","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-24T18:21:38Z","cross_cats_sorted":[],"title_canon_sha256":"b4acb25228bbb1caedba5c32d9f5f874d0989ee305259276e4069ce0f64ff72a","abstract_canon_sha256":"8fd77083618e836cbe8bcd3e526ad36612cb0a23c262ca6404094cf37a12c2f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:02.827152Z","signature_b64":"413jOTMiiruJSIV0+A+YACiKP37RcyL7Twu3EXtsZtjwRQ0riUzkSuaG25m9IOs3Y5HN7geu7Q/VcbUl301VBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a25b5c16c8b1018f9c642a2babb51809440b1d86a9b0e31406841a8fc5cde1db","last_reissued_at":"2026-05-18T00:04:02.826484Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:02.826484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The challenge of simultaneous object detection and pose estimation: a comparative study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Carolina Redondo-Cabrera, Daniel O\\~noro-Rubio, Pedro Gil-Jim\\'enez, Roberto J. L\\'opez-Sastre","submitted_at":"2018-01-24T18:21:38Z","abstract_excerpt":"Detecting objects and estimating their pose remains as one of the major challenges of the computer vision research community. There exists a compromise between localizing the objects and estimating their viewpoints. The detector ideally needs to be view-invariant, while the pose estimation process should be able to generalize towards the category-level. This work is an exploration of using deep learning models for solving both problems simultaneously. For doing so, we propose three novel deep learning architectures, which are able to perform a joint detection and pose estimation, where we grad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.08110","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":"1801.08110","created_at":"2026-05-18T00:04:02.826599+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.08110v1","created_at":"2026-05-18T00:04:02.826599+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.08110","created_at":"2026-05-18T00:04:02.826599+00:00"},{"alias_kind":"pith_short_12","alias_value":"UJNVYFWIWEAY","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"UJNVYFWIWEAY7HDE","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"UJNVYFWI","created_at":"2026-05-18T12:32:56.356000+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/UJNVYFWIWEAY7HDEFIV2XNIYBF","json":"https://pith.science/pith/UJNVYFWIWEAY7HDEFIV2XNIYBF.json","graph_json":"https://pith.science/api/pith-number/UJNVYFWIWEAY7HDEFIV2XNIYBF/graph.json","events_json":"https://pith.science/api/pith-number/UJNVYFWIWEAY7HDEFIV2XNIYBF/events.json","paper":"https://pith.science/paper/UJNVYFWI"},"agent_actions":{"view_html":"https://pith.science/pith/UJNVYFWIWEAY7HDEFIV2XNIYBF","download_json":"https://pith.science/pith/UJNVYFWIWEAY7HDEFIV2XNIYBF.json","view_paper":"https://pith.science/paper/UJNVYFWI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.08110&json=true","fetch_graph":"https://pith.science/api/pith-number/UJNVYFWIWEAY7HDEFIV2XNIYBF/graph.json","fetch_events":"https://pith.science/api/pith-number/UJNVYFWIWEAY7HDEFIV2XNIYBF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UJNVYFWIWEAY7HDEFIV2XNIYBF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UJNVYFWIWEAY7HDEFIV2XNIYBF/action/storage_attestation","attest_author":"https://pith.science/pith/UJNVYFWIWEAY7HDEFIV2XNIYBF/action/author_attestation","sign_citation":"https://pith.science/pith/UJNVYFWIWEAY7HDEFIV2XNIYBF/action/citation_signature","submit_replication":"https://pith.science/pith/UJNVYFWIWEAY7HDEFIV2XNIYBF/action/replication_record"}},"created_at":"2026-05-18T00:04:02.826599+00:00","updated_at":"2026-05-18T00:04:02.826599+00:00"}