Existing inpainting methods have achieved promising performance in recovering defected
images of specific scenes. However, filling holes involving multiple semantic categories …

Image and video inpainting is a classic problem in computer vision and computer graphics,
aiming to fill in the plausible and realistic content in the missing areas of images and videos …

Not all semantics become confusing when deploying a semantic segmentation model for
real-world scene understanding of adverse weather. The true semantics of most pixels have …

Interpolating video frames involving large motions remains an elusive challenge. In case
that frames involve small and fast-moving objects, conventional feed-forward neural network …

The rapid progression of convolutional neural networks (CNNs) has significantly improved
indoor scene parsing, transforming the fields of robotics, autonomous navigation …

Image inpainting for completing complicated semantic environments and diverse hole
patterns of corrupted images is challenging even for state-of-the-art learning-based …

In this work, we introduce a challenging image restoration task, referred to as SuperInpaint,
which aims to reconstruct missing regions in low-resolution images and generate completed …

Existing learning-based image inpainting methods are still in challenge when facing
complex semantic environments and diverse hole patterns. The prior information learned …

Deep convolutional neural networks (CNNs) with strong learning abilities have obtained
good results for image denoising. However, the CNNs for image denoising have …

In order to achieve interaction between structure and texture information in generative
adversarial image inpainting networks and improve the semantic veracity of the restored …