Deep learning has been highly successful in computer vision with large amounts of labeled
data, but struggles with limited labeled training data. To address this, Few-shot learning …

Practical object detection application can lose its effectiveness on image inputs with natural
distribution shifts. This problem leads the research community to pay more attention on the …

Object detection has achieved a huge breakthrough with deep neural networks and massive
annotated data. However, current detection methods cannot be directly transferred to the …

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a
few target labeled images are available for training in addition to sufficient source labeled …

The anatomical structure detection of fetal cardiac views is crucial for diagnosing fetal
congenital heart disease. In practice there is a large domain gap between different hospitals' …

Abstract Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of
segmenting novel categories from a distinct domain using only limited exemplars. In this …

For video models to be transferred and applied seamlessly across video tasks in varied
environments, Video Unsupervised Domain Adaptation (VUDA) has been introduced to …

Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting
from a labeled synthetic source domain to an unlabeled or sparsely labeled real target …

Few-shot segmentation performance declines substantially when facing images from a
domain different than the training domain effectively limiting real-world use cases. To …

Few-Shot transfer learning has become a major focus of research as it allows recognition of
new classes with limited labeled data. While it is assumed that train and test data have the …