Self-supervised pre-training has drawn increasing attention in recent years due to its
superior performance on numerous downstream tasks after fine-tuning. However, it is well …

Deep learning (DL) techniques have shown promising results in image compression
compared to conventional methods, with competitive bitrate and image reconstruction …

This paper provides a survey of the latest developments in visual signal coding and
processing with generative models. Specifically, our focus is on presenting the advancement …

The vulnerability of deep neural networks to adversarial perturbations has been widely
perceived in the computer vision community. From a security perspective, it poses a critical …

We study the robustness of learned image compression models against adversarial attacks
and present a training-free defense technique based on simple image transform functions …

Expression recognition is important in artificial intelligence research and has broad
application prospects in medical care and transportation. However, owing to differences in …

Adversarial attacks pose a huge challenge to the deployment of deep neural networks
(DNNs) in security-sensitive applications. Adversarial defense methods are developed to …

Abstract Neural Image Compression (NIC) has made significant strides in recent years.
However, the existing NIC methods demonstrate instability issues during iterative re …

Despite its recent advancements, multi-object tracking (MOT), one of the major research
areas in video technology, still faces various challenges, including severe occlusion and …

Nowadays, microscopy images are significant in medical research and clinical studies.
However, storage and transmission of data such as microscopy images are challenging …