Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

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

Most existing CNN-based super-resolution (SR) methods are developed based on an
assumption that the degradation is fixed and known (eg, bicubic downsampling). However …

In this paper, we address the problem of image anomaly detection and segmentation.
Anomaly detection involves making a binary decision as to whether an input image contains …

The goal of self-supervised learning from images is to construct image representations that
are semantically meaningful via pretext tasks that do not require semantic annotations. Many …

Can we automatically group images into semantically meaningful clusters when ground-
truth annotations are absent? The task of unsupervised image classification remains an …

Being able to learn dense semantic representations of images without supervision is an
important problem in computer vision. However, despite its significance, this problem …

Humans view the world through many sensory channels, eg, the long-wavelength light
channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …