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- W4387209220 abstract "In this work we propose a novel deep-learning approach for age estimation based on face images. We first introduce a dual image augmentation-aggregation approach based on attention. This allows the network to jointly utilize multiple face image augmentations whose embeddings are aggregated by a Transformer-Encoder. The resulting aggregated embedding is shown to better encode the face image attributes. We then propose a probabilistic hierarchical regression framework that combines a discrete probabilistic estimate of age labels, with a corresponding ensemble of regressors. Each regressor is particularly adapted and trained to refine the probabilistic estimate over a range of ages. Our scheme is shown to outperform contemporary schemes and provide a new state-of-the-art age estimation accuracy, when applied to the MORPH II dataset for age estimation. Last, we introduce a bias analysis of state-of-the-art age estimation results." @default.
- W4387209220 created "2023-09-30" @default.
- W4387209220 creator A5031057283 @default.
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- W4387209220 date "2021-03-17" @default.
- W4387209220 modified "2023-10-01" @default.
- W4387209220 title "Hierarchical Attention-based Age Estimation and Bias Estimation" @default.
- W4387209220 doi "https://doi.org/10.48550/arxiv.2103.09882" @default.
- W4387209220 hasPublicationYear "2021" @default.
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