{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OVYOWM275NBRHXBPPGV26UZHIB","short_pith_number":"pith:OVYOWM27","schema_version":"1.0","canonical_sha256":"7570eb335feb4313dc2f79abaf53274056773ce12ff9ba2b8417173e77c8690e","source":{"kind":"arxiv","id":"2508.01331","version":1},"attestation_state":"computed","paper":{"title":"Referring Remote Sensing Image Segmentation with Cross-view Semantics Interaction Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Huchuan Lu, Jiaxing Yang, Lihe Zhang","submitted_at":"2025-08-02T11:57:56Z","abstract_excerpt":"Recently, Referring Remote Sensing Image Segmentation (RRSIS) has aroused wide attention. To handle drastic scale variation of remote targets, existing methods only use the full image as input and nest the saliency-preferring techniques of cross-scale information interaction into traditional single-view structure. Although effective for visually salient targets, they still struggle in handling tiny, ambiguous ones in lots of real scenarios. In this work, we instead propose a paralleled yet unified segmentation framework Cross-view Semantics Interaction Network (CSINet) to solve the limitations"},"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":"2508.01331","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-02T11:57:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"eb03c6951aa8d5932c399b5ea056434f86e5714e550485f3a201e6b4a77716e6","abstract_canon_sha256":"721cb991fd873904712e021ed37f1a1095ef70f28f32f477482d56e179c5741d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:47:37.488816Z","signature_b64":"6NEXbm7C6abB5HmoOznHpcpzfrjC7F7VnSGdv5y9AsVT1n3gkMgyZQeMw1J3fWPyHLWq6HBnOXDPcuGB+RlNAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7570eb335feb4313dc2f79abaf53274056773ce12ff9ba2b8417173e77c8690e","last_reissued_at":"2026-07-05T11:47:37.488437Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:47:37.488437Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Referring Remote Sensing Image Segmentation with Cross-view Semantics Interaction Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Huchuan Lu, Jiaxing Yang, Lihe Zhang","submitted_at":"2025-08-02T11:57:56Z","abstract_excerpt":"Recently, Referring Remote Sensing Image Segmentation (RRSIS) has aroused wide attention. To handle drastic scale variation of remote targets, existing methods only use the full image as input and nest the saliency-preferring techniques of cross-scale information interaction into traditional single-view structure. Although effective for visually salient targets, they still struggle in handling tiny, ambiguous ones in lots of real scenarios. In this work, we instead propose a paralleled yet unified segmentation framework Cross-view Semantics Interaction Network (CSINet) to solve the limitations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.01331","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/2508.01331/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":"2508.01331","created_at":"2026-07-05T11:47:37.488493+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.01331v1","created_at":"2026-07-05T11:47:37.488493+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.01331","created_at":"2026-07-05T11:47:37.488493+00:00"},{"alias_kind":"pith_short_12","alias_value":"OVYOWM275NBR","created_at":"2026-07-05T11:47:37.488493+00:00"},{"alias_kind":"pith_short_16","alias_value":"OVYOWM275NBRHXBP","created_at":"2026-07-05T11:47:37.488493+00:00"},{"alias_kind":"pith_short_8","alias_value":"OVYOWM27","created_at":"2026-07-05T11:47:37.488493+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00987","citing_title":"An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OVYOWM275NBRHXBPPGV26UZHIB","json":"https://pith.science/pith/OVYOWM275NBRHXBPPGV26UZHIB.json","graph_json":"https://pith.science/api/pith-number/OVYOWM275NBRHXBPPGV26UZHIB/graph.json","events_json":"https://pith.science/api/pith-number/OVYOWM275NBRHXBPPGV26UZHIB/events.json","paper":"https://pith.science/paper/OVYOWM27"},"agent_actions":{"view_html":"https://pith.science/pith/OVYOWM275NBRHXBPPGV26UZHIB","download_json":"https://pith.science/pith/OVYOWM275NBRHXBPPGV26UZHIB.json","view_paper":"https://pith.science/paper/OVYOWM27","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.01331&json=true","fetch_graph":"https://pith.science/api/pith-number/OVYOWM275NBRHXBPPGV26UZHIB/graph.json","fetch_events":"https://pith.science/api/pith-number/OVYOWM275NBRHXBPPGV26UZHIB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OVYOWM275NBRHXBPPGV26UZHIB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OVYOWM275NBRHXBPPGV26UZHIB/action/storage_attestation","attest_author":"https://pith.science/pith/OVYOWM275NBRHXBPPGV26UZHIB/action/author_attestation","sign_citation":"https://pith.science/pith/OVYOWM275NBRHXBPPGV26UZHIB/action/citation_signature","submit_replication":"https://pith.science/pith/OVYOWM275NBRHXBPPGV26UZHIB/action/replication_record"}},"created_at":"2026-07-05T11:47:37.488493+00:00","updated_at":"2026-07-05T11:47:37.488493+00:00"}