Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …

Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

We propose an efficient method to ground pretrained text-only language models to the
visual domain, enabling them to process arbitrarily interleaved image-and-text data, and …

Text embeddings are useful features in many applications such as semantic search and
computing text similarity. Previous work typically trains models customized for different use …

Despite recent success in incorporating learning into point cloud registration, many works
focus on learning feature descriptors and continue to rely on nearest-neighbor feature …

The performance of face recognition system degrades when the variability of the acquired
faces increases. Prior work alleviates this issue by either monitoring the face quality in pre …

Emerging interests have been brought to recognize previously unseen objects given very
few training examples, known as few-shot object detection (FSOD). Recent researches …

Recent self-supervised methods for image representation learning are based on maximizing
the agreement between embedding vectors from different views of the same image. A trivial …

In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

Abstract Generative Adversarial Network (GAN) is a research hotspot in deep generative
models, which has been widely used in the field of medical image fusion. This paper …