Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources

Our goal is to design architectures that retain the groundbreaking performance of CNNs for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment... (read more)

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Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Face Alignment AFLW-Full Binary Face Alignment Mean NME 2.85 # 2

Methods used in the Paper


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