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- W2897335144 abstract "Compared with detection in still image, sequential face landmark detection in video is relatively less studied. In this work, we present a novel network with a dilated residual network and a residual convolutional LSTM to preserve the detection acuity at both spatial and temporal dimensions respectively. We also introduce and compare multi-label loss with regression loss and multi-class loss for face landmark detection. We perform extensive experiments to verify the contribution of different components. Our method achieves state-of-the-art performance on benchmark datasets." @default.
- W2897335144 created "2018-10-26" @default.
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- W2897335144 date "2018-07-01" @default.
- W2897335144 modified "2023-09-25" @default.
- W2897335144 title "Multi-Label Dilated Recurrent Network for Sequential Face Alignment" @default.
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- W2897335144 doi "https://doi.org/10.1109/icme.2018.8486438" @default.
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