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2D Wasserstein Loss for Robust Facial Landmark Detection

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Figure 1: An illustration of the Wasserstein loss between two 1D distributions. Standard L2 loss only considers the “activation” difference (point-wise value difference, vertical gray arrows), whereas the Wasserstein loss takes into account both the activa
Figure 2: Examples of HRNet detection on 300VW-S3.
Table 1: Numerical details of the facial landmark datasets and the Failure Rate (FR) of HRNet on each dataset.
Figure 4: Comparison of heatmap L2 loss and Wasserstein loss on 2D distributions. We observe that the value of L2 loss saturates when the two distributions do not overlap
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