{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5DYHI6JGOJR6HGIGNWW6QNIIIR","short_pith_number":"pith:5DYHI6JG","schema_version":"1.0","canonical_sha256":"e8f07479267263e399066dade8350844633326cae1176b6bae432e6fb6271b95","source":{"kind":"arxiv","id":"2607.20072","version":1},"attestation_state":"computed","paper":{"title":"Factor-Informed Uncertainty Distillation for Gaze Estimation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CV","authors_text":"Javier Andreu-Perez, Javier Fumanal Idocin, Mohammadreza Jamalifard, Parastoo Azizinezhad, Tom Foulsham, Yaxiong Lei","submitted_at":"2026-07-22T12:20:40Z","abstract_excerpt":"Deep gaze estimation works well in controlled capture but degrades in unconstrained settings, where systems must reject unreliable predictions. Single-pass uncertainty (e.g., heteroscedastic regression) infers uncertainty from pixels without explicit input-validity cues, while sampling based methods are often too costly for real time use. We propose Factor-Informed Uncertainty Distillation (FIUD), a teacher-student framework that aligns uncertainty with interpretable image-quality failure modes. A gradient-boosting teacher predicts expected gaze error from factors such as illumination, sharpne"},"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":"2607.20072","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-22T12:20:40Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"b918b17c757ffedfd13505bd6865e10da9747c8edc91b6e6c44d5983a9f42693","abstract_canon_sha256":"9c61e3c8c273bfb445f2928bfcbe3928d82f8d28ee8632a761e7892e70020333"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-23T01:24:59.148230Z","signature_b64":"Sko9rjjUCCOl5njieKdaBJqperOENtORYflbOGX/dFSLEc2KuATpYOq8w7RZEv9QN0WGhT+pCzX6NWCFxUuaAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8f07479267263e399066dade8350844633326cae1176b6bae432e6fb6271b95","last_reissued_at":"2026-07-23T01:24:59.147405Z","signature_status":"signed_v1","first_computed_at":"2026-07-23T01:24:59.147405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Factor-Informed Uncertainty Distillation for Gaze Estimation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CV","authors_text":"Javier Andreu-Perez, Javier Fumanal Idocin, Mohammadreza Jamalifard, Parastoo Azizinezhad, Tom Foulsham, Yaxiong Lei","submitted_at":"2026-07-22T12:20:40Z","abstract_excerpt":"Deep gaze estimation works well in controlled capture but degrades in unconstrained settings, where systems must reject unreliable predictions. Single-pass uncertainty (e.g., heteroscedastic regression) infers uncertainty from pixels without explicit input-validity cues, while sampling based methods are often too costly for real time use. We propose Factor-Informed Uncertainty Distillation (FIUD), a teacher-student framework that aligns uncertainty with interpretable image-quality failure modes. A gradient-boosting teacher predicts expected gaze error from factors such as illumination, sharpne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.20072","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/2607.20072/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":"2607.20072","created_at":"2026-07-23T01:24:59.147831+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.20072v1","created_at":"2026-07-23T01:24:59.147831+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.20072","created_at":"2026-07-23T01:24:59.147831+00:00"},{"alias_kind":"pith_short_12","alias_value":"5DYHI6JGOJR6","created_at":"2026-07-23T01:24:59.147831+00:00"},{"alias_kind":"pith_short_16","alias_value":"5DYHI6JGOJR6HGIG","created_at":"2026-07-23T01:24:59.147831+00:00"},{"alias_kind":"pith_short_8","alias_value":"5DYHI6JG","created_at":"2026-07-23T01:24:59.147831+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/5DYHI6JGOJR6HGIGNWW6QNIIIR","json":"https://pith.science/pith/5DYHI6JGOJR6HGIGNWW6QNIIIR.json","graph_json":"https://pith.science/api/pith-number/5DYHI6JGOJR6HGIGNWW6QNIIIR/graph.json","events_json":"https://pith.science/api/pith-number/5DYHI6JGOJR6HGIGNWW6QNIIIR/events.json","paper":"https://pith.science/paper/5DYHI6JG"},"agent_actions":{"view_html":"https://pith.science/pith/5DYHI6JGOJR6HGIGNWW6QNIIIR","download_json":"https://pith.science/pith/5DYHI6JGOJR6HGIGNWW6QNIIIR.json","view_paper":"https://pith.science/paper/5DYHI6JG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.20072&json=true","fetch_graph":"https://pith.science/api/pith-number/5DYHI6JGOJR6HGIGNWW6QNIIIR/graph.json","fetch_events":"https://pith.science/api/pith-number/5DYHI6JGOJR6HGIGNWW6QNIIIR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5DYHI6JGOJR6HGIGNWW6QNIIIR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5DYHI6JGOJR6HGIGNWW6QNIIIR/action/storage_attestation","attest_author":"https://pith.science/pith/5DYHI6JGOJR6HGIGNWW6QNIIIR/action/author_attestation","sign_citation":"https://pith.science/pith/5DYHI6JGOJR6HGIGNWW6QNIIIR/action/citation_signature","submit_replication":"https://pith.science/pith/5DYHI6JGOJR6HGIGNWW6QNIIIR/action/replication_record"}},"created_at":"2026-07-23T01:24:59.147831+00:00","updated_at":"2026-07-23T01:24:59.147831+00:00"}