{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HZXI57NMKNUJF3HP74CMYZMIZF","short_pith_number":"pith:HZXI57NM","schema_version":"1.0","canonical_sha256":"3e6e8efdac536892ecefff04cc6588c95a322082623bd4bf8c21094b91291c15","source":{"kind":"arxiv","id":"2312.04548","version":1},"attestation_state":"computed","paper":{"title":"Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Aritra Dutta, Jacob Nielsen, Mubarak Shah, Rajatsubhra Chakraborty, Srijan Das","submitted_at":"2023-12-07T18:59:14Z","abstract_excerpt":"Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally, the color content of the scenes, solar-zenith angle, and population density of different geographies influence the data diversity. These two factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground-view data, including the open-world foundational models.\n  To pave the way"},"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":"2312.04548","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-07T18:59:14Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"e9017084cc8def1d32c0ec539068541dbd0ef5af009028aa3aa5bb3408691f63","abstract_canon_sha256":"66ba9ba45b46b9e2d12765de543d880e4ee6aaa28e91f06bcac63e33fb37b686"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:21:35.859696Z","signature_b64":"6EsMJaBYGsrhix51fIunJOScglQBUZReqCHB0zndzDIErzhXC8wc3uqjzieyqwvndTMkl5jIPH+5OodXXDMPBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e6e8efdac536892ecefff04cc6588c95a322082623bd4bf8c21094b91291c15","last_reissued_at":"2026-07-05T07:21:35.859163Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:21:35.859163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Aritra Dutta, Jacob Nielsen, Mubarak Shah, Rajatsubhra Chakraborty, Srijan Das","submitted_at":"2023-12-07T18:59:14Z","abstract_excerpt":"Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally, the color content of the scenes, solar-zenith angle, and population density of different geographies influence the data diversity. These two factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground-view data, including the open-world foundational models.\n  To pave the way"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.04548","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/2312.04548/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":"2312.04548","created_at":"2026-07-05T07:21:35.859223+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.04548v1","created_at":"2026-07-05T07:21:35.859223+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.04548","created_at":"2026-07-05T07:21:35.859223+00:00"},{"alias_kind":"pith_short_12","alias_value":"HZXI57NMKNUJ","created_at":"2026-07-05T07:21:35.859223+00:00"},{"alias_kind":"pith_short_16","alias_value":"HZXI57NMKNUJF3HP","created_at":"2026-07-05T07:21:35.859223+00:00"},{"alias_kind":"pith_short_8","alias_value":"HZXI57NM","created_at":"2026-07-05T07:21:35.859223+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27876","citing_title":"SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27876","citing_title":"SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HZXI57NMKNUJF3HP74CMYZMIZF","json":"https://pith.science/pith/HZXI57NMKNUJF3HP74CMYZMIZF.json","graph_json":"https://pith.science/api/pith-number/HZXI57NMKNUJF3HP74CMYZMIZF/graph.json","events_json":"https://pith.science/api/pith-number/HZXI57NMKNUJF3HP74CMYZMIZF/events.json","paper":"https://pith.science/paper/HZXI57NM"},"agent_actions":{"view_html":"https://pith.science/pith/HZXI57NMKNUJF3HP74CMYZMIZF","download_json":"https://pith.science/pith/HZXI57NMKNUJF3HP74CMYZMIZF.json","view_paper":"https://pith.science/paper/HZXI57NM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.04548&json=true","fetch_graph":"https://pith.science/api/pith-number/HZXI57NMKNUJF3HP74CMYZMIZF/graph.json","fetch_events":"https://pith.science/api/pith-number/HZXI57NMKNUJF3HP74CMYZMIZF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HZXI57NMKNUJF3HP74CMYZMIZF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HZXI57NMKNUJF3HP74CMYZMIZF/action/storage_attestation","attest_author":"https://pith.science/pith/HZXI57NMKNUJF3HP74CMYZMIZF/action/author_attestation","sign_citation":"https://pith.science/pith/HZXI57NMKNUJF3HP74CMYZMIZF/action/citation_signature","submit_replication":"https://pith.science/pith/HZXI57NMKNUJF3HP74CMYZMIZF/action/replication_record"}},"created_at":"2026-07-05T07:21:35.859223+00:00","updated_at":"2026-07-05T07:21:35.859223+00:00"}