Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

Images can be described as visual representations or likeness of something (person or
object) which can be reproduced or captured, eg a hand drawing, photographic material …

Unsupervised domain adaptation has attracted growing research attention on semantic
segmentation. However, 1) most existing models cannot be directly applied into lesions …

Abstract Domain knowledge of face shapes and structures plays an important role in face
inpainting. However, general inpainting methods focus mainly on the resolution of …

Unsupervised domain adaptation without consuming annotation process for unlabeled
target data attracts appealing interests in semantic segmentation. However, 1) existing …

Weakly-supervised learning has attracted growing research attention on medical lesions
segmentation due to significant saving in pixel-level annotation cost. However, 1) most …

Deep learning (DL) models, eg, state-of-the-art convolutional neural networks (CNNs), have
been widely applied into security sensitivity tasks, such as face payment, security …

Dilated convolution kernels are constrained by their shared dilation, keeping them from
being aware of diverse spatial contents at different locations. We address such limitations by …

Facial image inpainting is a problem that is widely studied, and in recent years the
introduction of Generative Adversarial Networks, has led to improvements in the field …

Pose variation and occlusion are two key factors that affect the accuracy of face recognition.
Most of the previous work alleviate the impacts of pose and occlusion by performing the …