Browsing by Author "Wan, Sheng"

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CONFNET: PREDICT WITH CONFIDENCE
(2018-01-01)
Wan, Sheng
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Wu, Tung-Yu
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Wong, Wing H.
;
Lee, Chen-Yi
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電子工程學系及電子研究所
In this paper, we propose Confidence Network (ConfNet) which not only makes predictions on input images but also generates a confidence score that estimates the probability of correctness of each prediction. Furthermore, Confidence Loss is proposed to make ConfNet automatically learn confidence scores in the training phase. The experiments on two public datasets show that the confidence scores generated by ConfNet are highly correlated with the model accuracy and outperforms two related methods. When stacking two ConfNets in a cascade structure, 3.8x computational cost can be saved compared to the single state-of-the-art model with only 0.1% increase of error rate.
QuatNet: Quaternion-Based Head Pose Estimation With Multiregression Loss
(2019-04-01)
Hsu, Heng-Wei
;
Wu, Tung-Yu
;
Wan, Sheng
;
Wong, Wing Hung
;
Lee, Chen-Yi
Head pose estimation has attracted immense research interest recently, as its inherent information significantly improves the performance of face-related applications such as face alignment and face recognition. In this paper, we conduct an in-depth study of head pose estimation and present a multiregression loss function, an L2 regression loss combined with an ordinal regression loss, to train a convolutional neural network (CNN) that is dedicated to estimating head poses from RGB images without depth information. The ordinal regression loss is utilized to address the nonstationary property observed as the facial features change with respect to different head pose angles and learn robust features. The L2 regression loss leverages these features to provide precise angle predictions for input images. To avoid the ambiguity problem in the commonly used Euler angle representation, we further formulate the head pose estimation problem in quaternions. Our quaternion-based multiregression loss method achieves state-of-the-art performance on the AFLW2000, AFLW test set, and AFW datasets and is closing the gap with methods that utilize depth information on the BIWI dataset.