A mixture model with adaptive KDE and per-image cross-validation raises estimated human fixation consistency by 5-15% median log-likelihood and up to 2 AUC points over fixed-bandwidth Gaussian baselines.
IEEE Transactions on pattern analysis and machine intelligence 20(11), 1254–1259 (1998)
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A two-stage CNN model generates multi-scale gaze direction fields before regressing gaze heatmaps and introduces a new video dataset annotated by in-scene observers, outperforming prior methods.
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Raising the Ceiling: Better Empirical Fixation Densities for Saliency Benchmarking
A mixture model with adaptive KDE and per-image cross-validation raises estimated human fixation consistency by 5-15% median log-likelihood and up to 2 AUC points over fixed-bandwidth Gaussian baselines.
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Believe It or Not, We Know What You Are Looking at!
A two-stage CNN model generates multi-scale gaze direction fields before regressing gaze heatmaps and introduces a new video dataset annotated by in-scene observers, outperforming prior methods.