Deep learning has revolutionized the field of artificial intelligence. Based on the statistical
correlations uncovered by deep learning-based methods, computer vision tasks, such as …

Abstract Video Frame Interpolation (VFI) which aims at generating high-frame-rate videos
from low-frame-rate inputs is a highly challenging task. The emergence of bio-inspired …

Video deblurring aims to restore sharp frames from blurred video clips. Despite notable
progress in video deblurring works it is still a challenging problem because of the loss of …

In the realm of video object segmentation (VOS) the challenge of operating under low-light
conditions persists resulting in notably degraded image quality and compromised accuracy …

Despite significant progress made in image semantic segmentation, most research has
primarily focused on daytime scenes. Semantic segmentation of nighttime images is equally …

Synthetic data (ie, source domain) have been widely adopted to improve the semantic
segmentation performance for real-world images (ie, target domain), since obtaining pixel …

Nighttime semantic segmentation is essential for various applications, eg, autonomous
driving, which often faces challenges due to poor illumination and the lack of well-annotated …

In Federated Learning (FL), multiple clients collaboratively train a global model without
sharing private data. In semantic segmentation, the Federated source Free Domain …

Moving object segmentation (MOS) in dynamic scenes is challenging for autonomous
driving, especially for sequences obtained from moving ego vehicles. Most state-of-the-art …

Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source
domain to perform well on a target domain without requiring additional annotations. In the …