{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:5LRM6Z2DD2VID3VAFARDB6TPDJ","short_pith_number":"pith:5LRM6Z2D","schema_version":"1.0","canonical_sha256":"eae2cf67431eaa81eea0282230fa6f1a7d13eafdf1aff0bb7394f069523d8cb2","source":{"kind":"arxiv","id":"2211.15595","version":3},"attestation_state":"computed","paper":{"title":"FsaNet: Frequency Self-attention for Semantic Segmentation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ashkan Panahi, Fengyu Zhang, Guangjun Gao","submitted_at":"2022-11-28T17:49:46Z","abstract_excerpt":"Considering the spectral properties of images, we propose a new self-attention mechanism with highly reduced computational complexity, up to a linear rate. To better preserve edges while promoting similarity within objects, we propose individualized processes over different frequency bands. In particular, we study a case where the process is merely over low-frequency components. By ablation study, we show that low frequency self-attention can achieve very close or better performance relative to full frequency even without retraining the network. Accordingly, we design and embed novel plug-and-"},"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":"2211.15595","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-28T17:49:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b024eb7c034e012e9914419481d0e256de573e670a5666292f63fde675dc9371","abstract_canon_sha256":"13ebc7e338d0a63ed2982af75f71e6a09f63623370c814516d1f9b618c5cc091"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:34:46.534338Z","signature_b64":"uvPz+nTrleB9915j7xaH+13Z5dDE4vJLr6UNR44nlRYJ4vRTW6FJjSc5AGpfhwfeR+ucXC7fOU7OSc8lWfT3AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eae2cf67431eaa81eea0282230fa6f1a7d13eafdf1aff0bb7394f069523d8cb2","last_reissued_at":"2026-07-05T06:34:46.533888Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:34:46.533888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FsaNet: Frequency Self-attention for Semantic Segmentation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ashkan Panahi, Fengyu Zhang, Guangjun Gao","submitted_at":"2022-11-28T17:49:46Z","abstract_excerpt":"Considering the spectral properties of images, we propose a new self-attention mechanism with highly reduced computational complexity, up to a linear rate. To better preserve edges while promoting similarity within objects, we propose individualized processes over different frequency bands. In particular, we study a case where the process is merely over low-frequency components. By ablation study, we show that low frequency self-attention can achieve very close or better performance relative to full frequency even without retraining the network. Accordingly, we design and embed novel plug-and-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.15595","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/2211.15595/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":"2211.15595","created_at":"2026-07-05T06:34:46.533951+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.15595v3","created_at":"2026-07-05T06:34:46.533951+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.15595","created_at":"2026-07-05T06:34:46.533951+00:00"},{"alias_kind":"pith_short_12","alias_value":"5LRM6Z2DD2VI","created_at":"2026-07-05T06:34:46.533951+00:00"},{"alias_kind":"pith_short_16","alias_value":"5LRM6Z2DD2VID3VA","created_at":"2026-07-05T06:34:46.533951+00:00"},{"alias_kind":"pith_short_8","alias_value":"5LRM6Z2D","created_at":"2026-07-05T06:34:46.533951+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/5LRM6Z2DD2VID3VAFARDB6TPDJ","json":"https://pith.science/pith/5LRM6Z2DD2VID3VAFARDB6TPDJ.json","graph_json":"https://pith.science/api/pith-number/5LRM6Z2DD2VID3VAFARDB6TPDJ/graph.json","events_json":"https://pith.science/api/pith-number/5LRM6Z2DD2VID3VAFARDB6TPDJ/events.json","paper":"https://pith.science/paper/5LRM6Z2D"},"agent_actions":{"view_html":"https://pith.science/pith/5LRM6Z2DD2VID3VAFARDB6TPDJ","download_json":"https://pith.science/pith/5LRM6Z2DD2VID3VAFARDB6TPDJ.json","view_paper":"https://pith.science/paper/5LRM6Z2D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.15595&json=true","fetch_graph":"https://pith.science/api/pith-number/5LRM6Z2DD2VID3VAFARDB6TPDJ/graph.json","fetch_events":"https://pith.science/api/pith-number/5LRM6Z2DD2VID3VAFARDB6TPDJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5LRM6Z2DD2VID3VAFARDB6TPDJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5LRM6Z2DD2VID3VAFARDB6TPDJ/action/storage_attestation","attest_author":"https://pith.science/pith/5LRM6Z2DD2VID3VAFARDB6TPDJ/action/author_attestation","sign_citation":"https://pith.science/pith/5LRM6Z2DD2VID3VAFARDB6TPDJ/action/citation_signature","submit_replication":"https://pith.science/pith/5LRM6Z2DD2VID3VAFARDB6TPDJ/action/replication_record"}},"created_at":"2026-07-05T06:34:46.533951+00:00","updated_at":"2026-07-05T06:34:46.533951+00:00"}