{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3KG5VOGJKNFA2L5KPJZVTJWZZE","short_pith_number":"pith:3KG5VOGJ","schema_version":"1.0","canonical_sha256":"da8ddab8c9534a0d2faa7a7359a6d9c91d1966d4920a167a9c99c5e651df4dc3","source":{"kind":"arxiv","id":"2205.02887","version":1},"attestation_state":"computed","paper":{"title":"Evaluating Context for Deep Object Detectors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jan C. van Gemert, Osman Semih Kayhan","submitted_at":"2022-05-05T18:48:29Z","abstract_excerpt":"Which object detector is suitable for your context sensitive task? Deep object detectors exploit scene context for recognition differently. In this paper, we group object detectors into 3 categories in terms of context use: no context by cropping the input (RCNN), partial context by cropping the featuremap (two-stage methods) and full context without any cropping (single-stage methods). We systematically evaluate the effect of context for each deep detector category. We create a fully controlled dataset for varying context and investigate the context for deep detectors. We also evaluate gradua"},"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":"2205.02887","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-05-05T18:48:29Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"b9d52ab9bc9bb33d35a2751e6466b61747b0e0a6e15b00986e3f8071f379a706","abstract_canon_sha256":"2340041d4e6e42bd968affa546d9dd7de4e70feb4854b86f4cfd01097a15019a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:46.032477Z","signature_b64":"HiS5ADKySR0ZQC/LVMoc5M+HHluvsvOI3GD2oF4wPCH2TlKccIKZ0+mF4p0EaDumQ2fqj400sRQSd3Wu8tuSAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da8ddab8c9534a0d2faa7a7359a6d9c91d1966d4920a167a9c99c5e651df4dc3","last_reissued_at":"2026-07-05T04:20:46.032113Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:46.032113Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Context for Deep Object Detectors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jan C. van Gemert, Osman Semih Kayhan","submitted_at":"2022-05-05T18:48:29Z","abstract_excerpt":"Which object detector is suitable for your context sensitive task? Deep object detectors exploit scene context for recognition differently. In this paper, we group object detectors into 3 categories in terms of context use: no context by cropping the input (RCNN), partial context by cropping the featuremap (two-stage methods) and full context without any cropping (single-stage methods). We systematically evaluate the effect of context for each deep detector category. We create a fully controlled dataset for varying context and investigate the context for deep detectors. We also evaluate gradua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.02887","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.02887/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":"2205.02887","created_at":"2026-07-05T04:20:46.032174+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.02887v1","created_at":"2026-07-05T04:20:46.032174+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.02887","created_at":"2026-07-05T04:20:46.032174+00:00"},{"alias_kind":"pith_short_12","alias_value":"3KG5VOGJKNFA","created_at":"2026-07-05T04:20:46.032174+00:00"},{"alias_kind":"pith_short_16","alias_value":"3KG5VOGJKNFA2L5K","created_at":"2026-07-05T04:20:46.032174+00:00"},{"alias_kind":"pith_short_8","alias_value":"3KG5VOGJ","created_at":"2026-07-05T04:20:46.032174+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/3KG5VOGJKNFA2L5KPJZVTJWZZE","json":"https://pith.science/pith/3KG5VOGJKNFA2L5KPJZVTJWZZE.json","graph_json":"https://pith.science/api/pith-number/3KG5VOGJKNFA2L5KPJZVTJWZZE/graph.json","events_json":"https://pith.science/api/pith-number/3KG5VOGJKNFA2L5KPJZVTJWZZE/events.json","paper":"https://pith.science/paper/3KG5VOGJ"},"agent_actions":{"view_html":"https://pith.science/pith/3KG5VOGJKNFA2L5KPJZVTJWZZE","download_json":"https://pith.science/pith/3KG5VOGJKNFA2L5KPJZVTJWZZE.json","view_paper":"https://pith.science/paper/3KG5VOGJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.02887&json=true","fetch_graph":"https://pith.science/api/pith-number/3KG5VOGJKNFA2L5KPJZVTJWZZE/graph.json","fetch_events":"https://pith.science/api/pith-number/3KG5VOGJKNFA2L5KPJZVTJWZZE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3KG5VOGJKNFA2L5KPJZVTJWZZE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3KG5VOGJKNFA2L5KPJZVTJWZZE/action/storage_attestation","attest_author":"https://pith.science/pith/3KG5VOGJKNFA2L5KPJZVTJWZZE/action/author_attestation","sign_citation":"https://pith.science/pith/3KG5VOGJKNFA2L5KPJZVTJWZZE/action/citation_signature","submit_replication":"https://pith.science/pith/3KG5VOGJKNFA2L5KPJZVTJWZZE/action/replication_record"}},"created_at":"2026-07-05T04:20:46.032174+00:00","updated_at":"2026-07-05T04:20:46.032174+00:00"}