Deep learning architectures are now pervasive and filled almost all applications under
image processing, computer vision, and biometrics. The attractive property of feature …

The past decade has witnessed the great success of deep learning in many disciplines,
especially in computer vision and image processing. However, deep learning-based video …

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

Semantic communications has received growing interest since it can remarkably reduce the
amount of data to be transmitted without missing critical information. Most existing works …

Video self-supervised learning is a challenging task, which requires significant expressive
power from the model to leverage rich spatial-temporal knowledge and generate effective …

Deep neural networks have achieved remarkable success in computer vision tasks. Existing
neural networks mainly operate in the spatial domain with fixed input sizes. For practical …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

We present a learned image compression system based on GANs, operating at extremely
low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi …

At present, and increasingly so in the future, much of the captured visual content will not be
seen by humans. Instead, it will be used for automated machine vision analytics and may …

In this work, we address the problem of unsupervised domain adaptation for person re-ID
where annotations are available for the source domain but not for target. Previous methods …