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Register a new userNoise-Aware Saliency Prediction for Videos with Incomplete Gaze Data
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Added by
Iuri Frosio
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Deep-learning-based algorithms have led to impressive results in visual-saliency prediction, but the impact of noise in training gaze data has been largely overlooked. This issue is especially relevant for videos, where the gaze data tends to be incomplete, and thus noisier, compared to images. Therefore, we propose a noise-aware training (NAT) paradigm for visual-saliency prediction that quantifies the uncertainty arising from gaze data incompleteness and inaccuracy, and accounts for it in training. We demonstrate the advantage of NAT independently of the adopted model architecture, loss function, or training dataset. Given its robustness to the noise in incomplete training datasets, NAT ushers in the possibility of designing gaze datasets with fewer human subjects. We also introduce the first dataset that offers a video-game context for video-saliency research, with rich temporal semantics, and multiple gaze attractors per frame.
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Traditional gaze estimation methods typically require explicit user calibration to achieve high accuracy. This process is cumbersome and recalibration is often required when there are changes in factors such as illumination and pose. To address this challenge, we introduce SalGaze, a framework that utilizes saliency information in the visual content to transparently adapt the gaze estimation algorithm to the user without explicit user calibration. We design an algorithm to transform a saliency map into a differentiable loss map that can be used for the optimization of CNN-based models. SalGaze is also able to greatly augment standard point calibration data with implicit video saliency calibration data using a unified framework. We show accuracy improvements over 24% using our technique on existing methods.
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