{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HTQEVAAFV7JRKILEEK7YT726AL","short_pith_number":"pith:HTQEVAAF","schema_version":"1.0","canonical_sha256":"3ce04a8005afd315216422bf89ff5e02e91798e20d3008dc41fc2be6de7314c1","source":{"kind":"arxiv","id":"2510.21605","version":3},"attestation_state":"computed","paper":{"title":"S3OD: Towards Generalizable Salient Object Detection with Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christian Rupprecht, Hirokatsu Kataoka, Orest Kupyn","submitted_at":"2025-10-24T16:10:09Z","abstract_excerpt":"Salient object detection exemplifies data-bounded tasks where expensive pixel-precise annotations force separate model training for related subtasks like DIS and HR-SOD. We present a method that dramatically improves generalization through large-scale synthetic data generation and ambiguity-aware architecture. We introduce S3OD, a dataset of over 139,000 high-resolution images created through our multi-modal diffusion pipeline that extracts labels from diffusion and DINO-v3 features. The iterative generation framework prioritizes challenging categories based on model performance. We propose a "},"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":"2510.21605","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-10-24T16:10:09Z","cross_cats_sorted":[],"title_canon_sha256":"9d123dc952eaea50fbe8ae46e2cc84eb6957d607cc4c45116e59a58fd1c5ff21","abstract_canon_sha256":"a9a5e2ab88830833db4e3b93f7960ceb6c79077d45325e938fabe8439571c32c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:04.460743Z","signature_b64":"FXSd3dBEOxRKnzMzXuSU8a46tv3upUnfbReUuoBeOv0d3k+CbVxIzGwOGF32wwUubcsm7MK5azmDJ0WHUdOgAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ce04a8005afd315216422bf89ff5e02e91798e20d3008dc41fc2be6de7314c1","last_reissued_at":"2026-06-19T16:12:04.460274Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:04.460274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"S3OD: Towards Generalizable Salient Object Detection with Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christian Rupprecht, Hirokatsu Kataoka, Orest Kupyn","submitted_at":"2025-10-24T16:10:09Z","abstract_excerpt":"Salient object detection exemplifies data-bounded tasks where expensive pixel-precise annotations force separate model training for related subtasks like DIS and HR-SOD. We present a method that dramatically improves generalization through large-scale synthetic data generation and ambiguity-aware architecture. We introduce S3OD, a dataset of over 139,000 high-resolution images created through our multi-modal diffusion pipeline that extracts labels from diffusion and DINO-v3 features. The iterative generation framework prioritizes challenging categories based on model performance. We propose a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.21605","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/2510.21605/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":"2510.21605","created_at":"2026-06-19T16:12:04.460350+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.21605v3","created_at":"2026-06-19T16:12:04.460350+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.21605","created_at":"2026-06-19T16:12:04.460350+00:00"},{"alias_kind":"pith_short_12","alias_value":"HTQEVAAFV7JR","created_at":"2026-06-19T16:12:04.460350+00:00"},{"alias_kind":"pith_short_16","alias_value":"HTQEVAAFV7JRKILE","created_at":"2026-06-19T16:12:04.460350+00:00"},{"alias_kind":"pith_short_8","alias_value":"HTQEVAAF","created_at":"2026-06-19T16:12:04.460350+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/HTQEVAAFV7JRKILEEK7YT726AL","json":"https://pith.science/pith/HTQEVAAFV7JRKILEEK7YT726AL.json","graph_json":"https://pith.science/api/pith-number/HTQEVAAFV7JRKILEEK7YT726AL/graph.json","events_json":"https://pith.science/api/pith-number/HTQEVAAFV7JRKILEEK7YT726AL/events.json","paper":"https://pith.science/paper/HTQEVAAF"},"agent_actions":{"view_html":"https://pith.science/pith/HTQEVAAFV7JRKILEEK7YT726AL","download_json":"https://pith.science/pith/HTQEVAAFV7JRKILEEK7YT726AL.json","view_paper":"https://pith.science/paper/HTQEVAAF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.21605&json=true","fetch_graph":"https://pith.science/api/pith-number/HTQEVAAFV7JRKILEEK7YT726AL/graph.json","fetch_events":"https://pith.science/api/pith-number/HTQEVAAFV7JRKILEEK7YT726AL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HTQEVAAFV7JRKILEEK7YT726AL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HTQEVAAFV7JRKILEEK7YT726AL/action/storage_attestation","attest_author":"https://pith.science/pith/HTQEVAAFV7JRKILEEK7YT726AL/action/author_attestation","sign_citation":"https://pith.science/pith/HTQEVAAFV7JRKILEEK7YT726AL/action/citation_signature","submit_replication":"https://pith.science/pith/HTQEVAAFV7JRKILEEK7YT726AL/action/replication_record"}},"created_at":"2026-06-19T16:12:04.460350+00:00","updated_at":"2026-06-19T16:12:04.460350+00:00"}