As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

Although image inpainting, or the art of repairing the old and deteriorated images, has been
around for many years, it has recently gained even more popularity, because of the recent …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Large-scale multimodal generative modeling has created milestones in text-to-image and
text-to-video generation. Its application to audio still lags behind for two main reasons: the …

By decomposing the image formation process into a sequential application of denoising
autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image …

Recent studies have shown the importance of modeling long-range interactions in the
inpainting problem. To achieve this goal, existing approaches exploit either standalone …

Modern image inpainting systems, despite the significant progress, often struggle with large
missing areas, complex geometric structures, and high-resolution images. We find that one …

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …

We propose PD-GAN, a probabilistic diverse GAN forimage inpainting. Given an input image
with arbitrary holeregions, PD-GAN produces multiple inpainting results withdiverse and …

In view of the faultiness that the existing image inpainting methods fail to make full use of the
complete region to predict the missing region features when the object features are seriously …