{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:R3TYENNOPPYRCTQ5ENWOZ7ILQS","short_pith_number":"pith:R3TYENNO","schema_version":"1.0","canonical_sha256":"8ee78235ae7bf1114e1d236cecfd0b848d3657b11984c171a18469dd0acdd340","source":{"kind":"arxiv","id":"2505.17665","version":1},"attestation_state":"computed","paper":{"title":"EMRA-proxy: Enhancing Multi-Class Region Semantic Segmentation in Remote Sensing Images with Attention Proxy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Dan Yu, Tingyue Tang, Xiaoyi Yang, Yichun Yu, Yuqing Lan, Zhihuan Xing","submitted_at":"2025-05-23T09:30:45Z","abstract_excerpt":"High-resolution remote sensing (HRRS) image segmentation is challenging due to complex spatial layouts and diverse object appearances. While CNNs excel at capturing local features, they struggle with long-range dependencies, whereas Transformers can model global context but often neglect local details and are computationally expensive.We propose a novel approach, Region-Aware Proxy Network (RAPNet), which consists of two components: Contextual Region Attention (CRA) and Global Class Refinement (GCR). Unlike traditional methods that rely on grid-based layouts, RAPNet operates at the region leve"},"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":"2505.17665","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-23T09:30:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9c28826cb24907cce9daa1b9b83bf985f64cc741f3938ade0717d0c86a3af9d3","abstract_canon_sha256":"190af4a35deee6e6d90eb268c03ff2d94b8d0d3f46ced05e0963d508bb5158c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:30.097248Z","signature_b64":"lIaPpoj50xMfnNP6HRWNmFtpzUDQ5cHJJSYlcrsdr6coWb/ZmtA73Lcl1jW2hr9u81CHT6+PetbhWcOaQcoYBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ee78235ae7bf1114e1d236cecfd0b848d3657b11984c171a18469dd0acdd340","last_reissued_at":"2026-07-05T11:08:30.096782Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:30.096782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EMRA-proxy: Enhancing Multi-Class Region Semantic Segmentation in Remote Sensing Images with Attention Proxy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Dan Yu, Tingyue Tang, Xiaoyi Yang, Yichun Yu, Yuqing Lan, Zhihuan Xing","submitted_at":"2025-05-23T09:30:45Z","abstract_excerpt":"High-resolution remote sensing (HRRS) image segmentation is challenging due to complex spatial layouts and diverse object appearances. While CNNs excel at capturing local features, they struggle with long-range dependencies, whereas Transformers can model global context but often neglect local details and are computationally expensive.We propose a novel approach, Region-Aware Proxy Network (RAPNet), which consists of two components: Contextual Region Attention (CRA) and Global Class Refinement (GCR). Unlike traditional methods that rely on grid-based layouts, RAPNet operates at the region leve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17665","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/2505.17665/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":"2505.17665","created_at":"2026-07-05T11:08:30.096840+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.17665v1","created_at":"2026-07-05T11:08:30.096840+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17665","created_at":"2026-07-05T11:08:30.096840+00:00"},{"alias_kind":"pith_short_12","alias_value":"R3TYENNOPPYR","created_at":"2026-07-05T11:08:30.096840+00:00"},{"alias_kind":"pith_short_16","alias_value":"R3TYENNOPPYRCTQ5","created_at":"2026-07-05T11:08:30.096840+00:00"},{"alias_kind":"pith_short_8","alias_value":"R3TYENNO","created_at":"2026-07-05T11:08:30.096840+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/R3TYENNOPPYRCTQ5ENWOZ7ILQS","json":"https://pith.science/pith/R3TYENNOPPYRCTQ5ENWOZ7ILQS.json","graph_json":"https://pith.science/api/pith-number/R3TYENNOPPYRCTQ5ENWOZ7ILQS/graph.json","events_json":"https://pith.science/api/pith-number/R3TYENNOPPYRCTQ5ENWOZ7ILQS/events.json","paper":"https://pith.science/paper/R3TYENNO"},"agent_actions":{"view_html":"https://pith.science/pith/R3TYENNOPPYRCTQ5ENWOZ7ILQS","download_json":"https://pith.science/pith/R3TYENNOPPYRCTQ5ENWOZ7ILQS.json","view_paper":"https://pith.science/paper/R3TYENNO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.17665&json=true","fetch_graph":"https://pith.science/api/pith-number/R3TYENNOPPYRCTQ5ENWOZ7ILQS/graph.json","fetch_events":"https://pith.science/api/pith-number/R3TYENNOPPYRCTQ5ENWOZ7ILQS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R3TYENNOPPYRCTQ5ENWOZ7ILQS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R3TYENNOPPYRCTQ5ENWOZ7ILQS/action/storage_attestation","attest_author":"https://pith.science/pith/R3TYENNOPPYRCTQ5ENWOZ7ILQS/action/author_attestation","sign_citation":"https://pith.science/pith/R3TYENNOPPYRCTQ5ENWOZ7ILQS/action/citation_signature","submit_replication":"https://pith.science/pith/R3TYENNOPPYRCTQ5ENWOZ7ILQS/action/replication_record"}},"created_at":"2026-07-05T11:08:30.096840+00:00","updated_at":"2026-07-05T11:08:30.096840+00:00"}