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- W4324286911 abstract "Recent advances in gaze estimation are mainly dominated by deep learning like other computer vision tasks. While these deep methods get top results on gaze within-datasets evaluation, the model performance degrades dramatically when generalized to the target domain. The poor generalization performance prevents the deep methods from practical application. In this paper, we seek to improve the generalization performance of the deep neural network on gaze estimation. We found gaze estimation is suffered from the over-fitting problem, and propose to alleviate the problem with the technique of regularization: a) Weight Decay, b) Feature Normalization, and c) Weight Smoothing. Without extra computing resources or network parameters, our methods surpass all the gaze generalization benchmarks in experiments and reach the performance of the state-of-the-art unsupervised gaze domain generalization approaches." @default.
- W4324286911 created "2023-03-16" @default.
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- W4324286911 date "2022-12-23" @default.
- W4324286911 modified "2023-09-23" @default.
- W4324286911 title "Revisit Regularization Techniques for Gaze Estimation Generalization" @default.
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- W4324286911 doi "https://doi.org/10.1145/3579109.3579125" @default.
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