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When Is a Corrupted Calibration Label Detectable?A Model-Relative Detectability Floor for Few-Shot Gaze Personalization

https://doi.org/10.2139/ssrn.7429762
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inferredcites →https://doi.org/10.1007/978-3-030-01249-6_21openalex-snapshot
RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments
inferredcites →https://doi.org/10.1007/978-3-030-01264-9_7openalex-snapshot
Appearance-Based Gaze Estimation via Evaluation-Guided Asymmetric Regression
inferredcites →https://doi.org/10.1007/978-3-030-20876-9_20openalex-snapshot
Appearance-Based Gaze Estimation Using Dilated-Convolutions
inferredcites →https://doi.org/10.1007/978-3-030-58558-7_22openalex-snapshot
ETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation
inferredcites →https://doi.org/10.1007/978-3-031-20053-3_8openalex-snapshot
Neighborhood Collective Estimation for Noisy Label Identification and Correction
inferredcites →https://doi.org/10.1016/j.patrec.2025.09.009openalex-snapshot
RoNFA: Robust neural field-based approach for few-shot image classification with noisy labels
inferredcites →https://doi.org/10.1109/lsp.2012.2227726openalex-snapshot
Making a “Completely Blind” Image Quality Analyzer
inferredcites →https://doi.org/10.1109/tip.2012.2214050openalex-snapshot
No-Reference Image Quality Assessment in the Spatial Domain
inferredcites →https://doi.org/10.1109/tnnls.2020.2984710openalex-snapshot
Robust Few-Shot Learning for User-Provided Data
inferredcites →https://doi.org/10.1109/tpami.2019.2957373openalex-snapshot
A Differential Approach for Gaze Estimation
inferredcites →https://doi.org/10.1613/jair.1.12125openalex-snapshot
Confident Learning: Estimating Uncertainty in Dataset Labels
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.