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• W3100972044 abstract "We present a new loss function for the validation of image landmarks detected via Convolutional Neural Networks (CNN). The network learns to estimate how accurate its landmark estimation is. This loss function is applicable to all regression-based location estimations and allows the exclusion of unreliable landmarks from further processing. In addition, we formulate a novel batch balancing approach which weights the importance of samples based on their produced loss. This is done by computing a probability distribution mapping on an interval from which samples can be selected using a uniform random selection scheme. We conducted experiments on the 300W, AFLW, and WFLW facial landmark datasets. In the first experiments, the influence of our batch balancing approach is evaluated by comparing it against uniform sampling. In addition, we evaluated the impact of the validation loss on the landmark accuracy based on uniform sampling. The last experiments evaluate the correlation of the validation signal with the landmark accuracy. All experiments were performed for all three datasets." @default.
• W3100972044 created "2020-11-23" @default.
• W3100972044 creator A5008809634 @default.
• W3100972044 creator A5015458683 @default.
• W3100972044 date "2020-01-31" @default.
• W3100972044 modified "2023-10-18" @default.
• W3100972044 title "Learning to validate the quality of detected landmarks" @default.
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• W3100972044 doi "https://doi.org/10.1117/12.2559517" @default.
• W3100972044 hasPublicationYear "2020" @default.
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