Medical image segmentation methods often rely on fully supervised approa...
This paper investigates the differences in data organization between
con...
Deep learning in computer vision has achieved great success with the pri...
Despite the popularization of deep neural networks (DNNs) in many fields...
Unsupervised domain adaptation addresses the problem of classifying data...
Micro-expression recognition has drawn increasing attention due to its w...
Space-time video super-resolution (STVSR) aims to construct a high space...
The point cloud representation of an object can have a large geometric
v...
Instance segmentation is of great importance for many biological
applica...
In this paper, we study an arguably least restrictive setting of domain
...
Unsupervised domain adaptation aims to learn a task classifier that perf...
Unsupervised domain adaptation (UDA) is to learn classification models t...
Scoliosis is a congenital disease that causes lateral curvature in the s...
Semi-supervised learning has recently been attracting attention as an
al...
Scoliosis is a congenital disease in which the spine is deformed from it...
Unsupervised domain adaptation (UDA) is to make predictions for unlabele...
In this paper, we study the formalism of unsupervised multi-class domain...
In this work, we propose to resolve the issue existing in current deep
l...
Given labeled instances on a source domain and unlabeled ones on a targe...
Instance segmentation of biological images is essential for studying obj...
Unsupervised domain adaptation aims to learn a model of classifier for
u...
Hand pose estimation from a monocular RGB image is an important but
chal...
With the introduction of fully convolutional neural networks, deep learn...
Radiation therapy (RT) is a common treatment for head and neck (HaN) can...
Fine-grained visual categorization (FGVC) is challenging due in part to ...