{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3V535LXQLHELFF2PLFFITSLCZS","short_pith_number":"pith:3V535LXQ","schema_version":"1.0","canonical_sha256":"dd7bbeaef059c8b2974f594a89c962cc9c1d46643e6dfc124e4edca661e5caca","source":{"kind":"arxiv","id":"2605.23790","version":1},"attestation_state":"computed","paper":{"title":"Exploring deep learning for Event-Based Saliency Prediction with a Transformer-based model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jean Martinet, Romaric Mazna, Sai Deepesh Pokala","submitted_at":"2026-05-22T15:52:01Z","abstract_excerpt":"Saliency prediction has been extensively studied in RGB images and videos as a computational model of human visual attention. In contrast, predicting saliency from event-based data remains largely unexplored, despite the biological inspiration and favorable sensing properties of event cameras. Two obstacles have held this direction back: the absence of large-scale event saliency datasets, and the lack of a strong baseline. In this paper, we introduce SEST (Swin Event-based Saliency Transformer), a transformer-based model for saliency prediction from event data, bridging the data scarcity barri"},"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":"2605.23790","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-22T15:52:01Z","cross_cats_sorted":[],"title_canon_sha256":"0ec1325078f04a04c29d86ca44499d9ed4a14481d39c5f5711d79dbdfe09ae16","abstract_canon_sha256":"a9f6571a8e130870dd46195bd00fdbc7719431d0f67fae4665a0ee5c6b234d4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:02:32.706205Z","signature_b64":"X4QOCDV9SVaoD0YccumTVtgSN2C7xtjPsn48PKOgDjLy85XlAoFxKPCHHVHcwb3/zHbs9T4ZxojEUHshLzmKCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd7bbeaef059c8b2974f594a89c962cc9c1d46643e6dfc124e4edca661e5caca","last_reissued_at":"2026-05-25T02:02:32.705498Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:02:32.705498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring deep learning for Event-Based Saliency Prediction with a Transformer-based model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jean Martinet, Romaric Mazna, Sai Deepesh Pokala","submitted_at":"2026-05-22T15:52:01Z","abstract_excerpt":"Saliency prediction has been extensively studied in RGB images and videos as a computational model of human visual attention. In contrast, predicting saliency from event-based data remains largely unexplored, despite the biological inspiration and favorable sensing properties of event cameras. Two obstacles have held this direction back: the absence of large-scale event saliency datasets, and the lack of a strong baseline. In this paper, we introduce SEST (Swin Event-based Saliency Transformer), a transformer-based model for saliency prediction from event data, bridging the data scarcity barri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23790","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/2605.23790/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":"2605.23790","created_at":"2026-05-25T02:02:32.705619+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.23790v1","created_at":"2026-05-25T02:02:32.705619+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23790","created_at":"2026-05-25T02:02:32.705619+00:00"},{"alias_kind":"pith_short_12","alias_value":"3V535LXQLHEL","created_at":"2026-05-25T02:02:32.705619+00:00"},{"alias_kind":"pith_short_16","alias_value":"3V535LXQLHELFF2P","created_at":"2026-05-25T02:02:32.705619+00:00"},{"alias_kind":"pith_short_8","alias_value":"3V535LXQ","created_at":"2026-05-25T02:02:32.705619+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/3V535LXQLHELFF2PLFFITSLCZS","json":"https://pith.science/pith/3V535LXQLHELFF2PLFFITSLCZS.json","graph_json":"https://pith.science/api/pith-number/3V535LXQLHELFF2PLFFITSLCZS/graph.json","events_json":"https://pith.science/api/pith-number/3V535LXQLHELFF2PLFFITSLCZS/events.json","paper":"https://pith.science/paper/3V535LXQ"},"agent_actions":{"view_html":"https://pith.science/pith/3V535LXQLHELFF2PLFFITSLCZS","download_json":"https://pith.science/pith/3V535LXQLHELFF2PLFFITSLCZS.json","view_paper":"https://pith.science/paper/3V535LXQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.23790&json=true","fetch_graph":"https://pith.science/api/pith-number/3V535LXQLHELFF2PLFFITSLCZS/graph.json","fetch_events":"https://pith.science/api/pith-number/3V535LXQLHELFF2PLFFITSLCZS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3V535LXQLHELFF2PLFFITSLCZS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3V535LXQLHELFF2PLFFITSLCZS/action/storage_attestation","attest_author":"https://pith.science/pith/3V535LXQLHELFF2PLFFITSLCZS/action/author_attestation","sign_citation":"https://pith.science/pith/3V535LXQLHELFF2PLFFITSLCZS/action/citation_signature","submit_replication":"https://pith.science/pith/3V535LXQLHELFF2PLFFITSLCZS/action/replication_record"}},"created_at":"2026-05-25T02:02:32.705619+00:00","updated_at":"2026-05-25T02:02:32.705619+00:00"}