{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:5JVV3AYW5BLLQYD44FXQ7MPN3L","short_pith_number":"pith:5JVV3AYW","schema_version":"1.0","canonical_sha256":"ea6b5d8316e856b8607ce16f0fb1eddadd66ecca7e84ab8fb4113f822c46e6b7","source":{"kind":"arxiv","id":"2001.02870","version":3},"attestation_state":"computed","paper":{"title":"Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Kaiqiang Chen, Kun Fu, Ruigang Niu, Wenhui Diao, Xian Sun, Yu Tian","submitted_at":"2020-01-09T07:47:51Z","abstract_excerpt":"Semantic segmentation in very high resolution (VHR) aerial images is one of the most challenging tasks in remote sensing image understanding. Most of the current approaches are based on deep convolutional neural networks (DCNNs). However, standard convolution with local receptive fields fails in modeling global dependencies. Prior researches have indicated that attention-based methods can capture long-range dependencies and further reconstruct the feature maps for better representation. Nevertheless, limited by the mere perspective of spacial and channel attention and huge computation complexi"},"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":"2001.02870","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-01-09T07:47:51Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"6ce78552ceb99f5f65eb215bce4e48580b17f10c3660735a2fef2c2a7e001ab1","abstract_canon_sha256":"67ce902f8f94c4522af0e8db4881be4110a4bea11c8b22cb726273ab2975c376"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:35:10.832914Z","signature_b64":"efAw/P0oHP7P83HKz+MZzD/W8phEmbiHfYF+WY2zku8SYEHaXBp5R5m6E0htbWdS0QMb2BeQg/9Rga6u1PCkDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea6b5d8316e856b8607ce16f0fb1eddadd66ecca7e84ab8fb4113f822c46e6b7","last_reissued_at":"2026-07-05T01:35:10.832446Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:35:10.832446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Kaiqiang Chen, Kun Fu, Ruigang Niu, Wenhui Diao, Xian Sun, Yu Tian","submitted_at":"2020-01-09T07:47:51Z","abstract_excerpt":"Semantic segmentation in very high resolution (VHR) aerial images is one of the most challenging tasks in remote sensing image understanding. Most of the current approaches are based on deep convolutional neural networks (DCNNs). However, standard convolution with local receptive fields fails in modeling global dependencies. Prior researches have indicated that attention-based methods can capture long-range dependencies and further reconstruct the feature maps for better representation. Nevertheless, limited by the mere perspective of spacial and channel attention and huge computation complexi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.02870","kind":"arxiv","version":3},"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/2001.02870/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":"2001.02870","created_at":"2026-07-05T01:35:10.832521+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.02870v3","created_at":"2026-07-05T01:35:10.832521+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.02870","created_at":"2026-07-05T01:35:10.832521+00:00"},{"alias_kind":"pith_short_12","alias_value":"5JVV3AYW5BLL","created_at":"2026-07-05T01:35:10.832521+00:00"},{"alias_kind":"pith_short_16","alias_value":"5JVV3AYW5BLLQYD4","created_at":"2026-07-05T01:35:10.832521+00:00"},{"alias_kind":"pith_short_8","alias_value":"5JVV3AYW","created_at":"2026-07-05T01:35:10.832521+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/5JVV3AYW5BLLQYD44FXQ7MPN3L","json":"https://pith.science/pith/5JVV3AYW5BLLQYD44FXQ7MPN3L.json","graph_json":"https://pith.science/api/pith-number/5JVV3AYW5BLLQYD44FXQ7MPN3L/graph.json","events_json":"https://pith.science/api/pith-number/5JVV3AYW5BLLQYD44FXQ7MPN3L/events.json","paper":"https://pith.science/paper/5JVV3AYW"},"agent_actions":{"view_html":"https://pith.science/pith/5JVV3AYW5BLLQYD44FXQ7MPN3L","download_json":"https://pith.science/pith/5JVV3AYW5BLLQYD44FXQ7MPN3L.json","view_paper":"https://pith.science/paper/5JVV3AYW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.02870&json=true","fetch_graph":"https://pith.science/api/pith-number/5JVV3AYW5BLLQYD44FXQ7MPN3L/graph.json","fetch_events":"https://pith.science/api/pith-number/5JVV3AYW5BLLQYD44FXQ7MPN3L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5JVV3AYW5BLLQYD44FXQ7MPN3L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5JVV3AYW5BLLQYD44FXQ7MPN3L/action/storage_attestation","attest_author":"https://pith.science/pith/5JVV3AYW5BLLQYD44FXQ7MPN3L/action/author_attestation","sign_citation":"https://pith.science/pith/5JVV3AYW5BLLQYD44FXQ7MPN3L/action/citation_signature","submit_replication":"https://pith.science/pith/5JVV3AYW5BLLQYD44FXQ7MPN3L/action/replication_record"}},"created_at":"2026-07-05T01:35:10.832521+00:00","updated_at":"2026-07-05T01:35:10.832521+00:00"}