Semantic Face Segmentation Using Convolutional Neural Networks With a Supervised Attention Module
Akiyoshi Hizukuri,
Yuto Hirata,
Ryohei Nakayama
Abstract:A self-attention module is often used in image segmentation tasks such as facial part segmentation. Because the self-attention module weights the features at each position using the weighted sum of features at all positions obtained by the middle layer of a convolutional neural network (CNN), the target regions for the segmentation might not be weighted sufficiently. The purpose of this study was to develop a semantic segmentation method for facial parts using a CNN with a supervised attention module that focu… Show more
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