{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6DYFEQJ4RXM6PMWJYJW7SDMHBB","short_pith_number":"pith:6DYFEQJ4","schema_version":"1.0","canonical_sha256":"f0f052413c8dd9e7b2c9c26df90d87087508bac879dd700cf6651ce5a9e336a5","source":{"kind":"arxiv","id":"2402.18402","version":2},"attestation_state":"computed","paper":{"title":"A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Francesco Barbato, Mehmet Kerim Yucel, Mete Ozay, Pietro Zanuttigh, Umberto Michieli","submitted_at":"2024-02-28T15:24:58Z","abstract_excerpt":"In multimedia understanding tasks, corrupted samples pose a critical challenge, because when fed to machine learning models they lead to performance degradation. In the past, three groups of approaches have been proposed to handle noisy data: i) enhancer and denoiser modules to improve the quality of the noisy data, ii) data augmentation approaches, and iii) domain adaptation strategies. All the aforementioned approaches come with drawbacks that limit their applicability; the first has high computational costs and requires pairs of clean-corrupted data for training, while the others only allow"},"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":"2402.18402","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-02-28T15:24:58Z","cross_cats_sorted":[],"title_canon_sha256":"a7a16ed541a5ece42efdfb8b7d4bc0e6bd7e93f985bb38df49c68183420698c4","abstract_canon_sha256":"b3d7a88943e6de4a1695ca45141019e404673694f2f7ef0a8166f2c052f0603c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:24.419820Z","signature_b64":"d4b/LDKSaLGPDdhri2LhfWmSXHfF9ETZjJyvMEAB6Irj6uqmU8KRzzInNa6T82z6cwizq6b/v7GZICEzng39Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0f052413c8dd9e7b2c9c26df90d87087508bac879dd700cf6651ce5a9e336a5","last_reissued_at":"2026-07-05T07:50:24.419353Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:24.419353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Francesco Barbato, Mehmet Kerim Yucel, Mete Ozay, Pietro Zanuttigh, Umberto Michieli","submitted_at":"2024-02-28T15:24:58Z","abstract_excerpt":"In multimedia understanding tasks, corrupted samples pose a critical challenge, because when fed to machine learning models they lead to performance degradation. In the past, three groups of approaches have been proposed to handle noisy data: i) enhancer and denoiser modules to improve the quality of the noisy data, ii) data augmentation approaches, and iii) domain adaptation strategies. All the aforementioned approaches come with drawbacks that limit their applicability; the first has high computational costs and requires pairs of clean-corrupted data for training, while the others only allow"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.18402","kind":"arxiv","version":2},"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/2402.18402/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":"2402.18402","created_at":"2026-07-05T07:50:24.419414+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.18402v2","created_at":"2026-07-05T07:50:24.419414+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.18402","created_at":"2026-07-05T07:50:24.419414+00:00"},{"alias_kind":"pith_short_12","alias_value":"6DYFEQJ4RXM6","created_at":"2026-07-05T07:50:24.419414+00:00"},{"alias_kind":"pith_short_16","alias_value":"6DYFEQJ4RXM6PMWJ","created_at":"2026-07-05T07:50:24.419414+00:00"},{"alias_kind":"pith_short_8","alias_value":"6DYFEQJ4","created_at":"2026-07-05T07:50:24.419414+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/6DYFEQJ4RXM6PMWJYJW7SDMHBB","json":"https://pith.science/pith/6DYFEQJ4RXM6PMWJYJW7SDMHBB.json","graph_json":"https://pith.science/api/pith-number/6DYFEQJ4RXM6PMWJYJW7SDMHBB/graph.json","events_json":"https://pith.science/api/pith-number/6DYFEQJ4RXM6PMWJYJW7SDMHBB/events.json","paper":"https://pith.science/paper/6DYFEQJ4"},"agent_actions":{"view_html":"https://pith.science/pith/6DYFEQJ4RXM6PMWJYJW7SDMHBB","download_json":"https://pith.science/pith/6DYFEQJ4RXM6PMWJYJW7SDMHBB.json","view_paper":"https://pith.science/paper/6DYFEQJ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.18402&json=true","fetch_graph":"https://pith.science/api/pith-number/6DYFEQJ4RXM6PMWJYJW7SDMHBB/graph.json","fetch_events":"https://pith.science/api/pith-number/6DYFEQJ4RXM6PMWJYJW7SDMHBB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6DYFEQJ4RXM6PMWJYJW7SDMHBB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6DYFEQJ4RXM6PMWJYJW7SDMHBB/action/storage_attestation","attest_author":"https://pith.science/pith/6DYFEQJ4RXM6PMWJYJW7SDMHBB/action/author_attestation","sign_citation":"https://pith.science/pith/6DYFEQJ4RXM6PMWJYJW7SDMHBB/action/citation_signature","submit_replication":"https://pith.science/pith/6DYFEQJ4RXM6PMWJYJW7SDMHBB/action/replication_record"}},"created_at":"2026-07-05T07:50:24.419414+00:00","updated_at":"2026-07-05T07:50:24.419414+00:00"}