The metaverse aims to build an immersive virtual reality world to support the daily life, work,
and recreation of people. In this survey, the status quo of the metaverse is investigated, and …

Vision transformer networks have shown superiority in many computer vision tasks. In this
paper, we take a step further by proposing a novel generative vision transformer with latent …

While researches on model-based blind single image super-resolution (SISR) have
achieved tremendous successes recently, most of them do not consider the image …

While deep learning (DL)-based video deraining methods have achieved significant
success recently, they still exist two major drawbacks. Firstly, most of them do not sufficiently …

We investigate learning feature-to-feature translator networks by alternating back-
propagation as a general-purpose solution to zero-shot learning (ZSL) problems. It is a …

Generative models that can model and predict sequences of future events can, in principle,
learn to capture complex real-world phenomena, such as physical interactions. However, a …

Video understanding calls for a model to learn the characteristic interplay between static
scene content and its dynamics: Given an image, the model must be able to predict a future …

In this paper, we propose a noise-aware encoder-decoder framework to disentangle a clean
saliency predictor from noisy training examples, where the noisy labels are generated by …

This paper studies the fundamental problem of learning deep generative models that consist
of multiple layers of latent variables organized in top-down architectures. Such models have …

Multivariate time series anomaly detection has become an active area of research in recent
years, with Deep Learning models outperforming previous approaches on benchmark …