Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

We show how transformers can be used to vastly simplify neural video compression.
Previous methods have been relying on an increasing number of architectural biases and …

Applying deep learning to video compression has attracted increasing attention in recent
few years. In this work, we address end-to-end learned video compression with a special …

We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video
transmission scheme that leverages the power of deep neural networks (DNNs) to directly …

A central challenge of video prediction lies where the system has to reason the object's
future motion from image frames while simultaneously maintaining the consistency of its …

Video anomaly detection aims to automatically identify unusual objects or behaviours by
learning from normal videos. Previous methods tend to use simplistic reconstruction or …

Intelligent video coding (IVC), which dates back to the late 1980s with the concept of
encoding videos with knowledge and semantics, includes visual content compact …

Inter prediction is the critical component in hybrid coding framework to deal with the
temporal redundancy. Most of the neural video coding methods typically follow the motion …

Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the
acquisition of digital content and impelling the progress of visual compression towards …