Although deep learning has achieved satisfactory performance in computer vision, a large
volume of images is required. However, collecting images is often expensive and …

Common image-to-image translation methods rely on joint training over data from both
source and target domains. The training process requires concurrent access to both …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …

Visual place recognition has attracted widespread research interest in multiple fields such
as computer vision and robotics. Recently, researchers have employed advanced deep …

Generative adversarial networks (GANs), a novel framework for training generative models
in an adversarial setup, have attracted significant attention in recent years. The two …

Translating images from a source domain to a target domain for learning target models is
one of the most common strategies in domain adaptive semantic segmentation (DASS) …

We focus on unsupervised domain adaptation (UDA) in image segmentation. Existing works
address this challenge largely by aligning inter-domain representations, which may lead …

Meta-learning is an effective tool to address the few-shot learning problem, which requires
new data to be classified considering only a few training examples. However, when used for …

Abstract Domain adaptation is an important task for medical image analysis to improve
generalization on datasets collected from diverse institutes using different scanners and …

Deep neural networks (DNNs) have proven their capabilities in the past years and play a
significant role in environment perception for the challenging application of automated …