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 …
Y Wang, CM Albrecht, NAA Braham… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While …
L Wang, Y Wang, X Dong, Q Xu… - Proceedings of the …, 2021 - openaccess.thecvf.com
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 …
J Yi, S Yoon - Proceedings of the Asian conference on …, 2020 - openaccess.thecvf.com
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 …
I Misra, L Maaten - … of the IEEE/CVF conference on …, 2020 - openaccess.thecvf.com
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 …