Semantic segmentation plays a fundamental role in a broad variety of computer vision
applications, providing key information for the global understanding of an image. Yet, the …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

We propose an approach to domain adaptation for semantic segmentation that is both
practical and highly accurate. In contrast to previous work, we abandon the use of …

This paper focuses on the unsupervised domain adaptation of transferring the knowledge
from the source domain to the target domain in the context of semantic segmentation …

Unsupervised domain adaptation (DA) has gained substantial interest in semantic
segmentation. However, almost all prior arts assume concurrent access to both labeled …

Panoramic images with their 360deg directional view encompass exhaustive information
about the surrounding space, providing a rich foundation for scene understanding. To unfold …

Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to
transfer the knowledge from source to target under unaligned label space. The main …

Federated learning-based semantic segmentation (FSS) has drawn widespread attention
via decentralized training on local clients. However, most FSS models assume categories …

We present an approach to enhancing the realism of synthetic images. The images are
enhanced by a convolutional network that leverages intermediate representations produced …