Self-supervised learning (SSL) has gained wide-spread attention in the remote sensing (RS)
and Earth observation (EO) communities owing to its ability to learn task-agnostic …

Self-supervised representation learning (SSL) typically suffers from inadequate data
utilization and feature-specificity due to the suboptimal sampling strategy and the …

Existing mirror detection methods require supervised ImageNet pre-training to obtain good
general-purpose image features. However, supervised ImageNet pre-training focuses on …

Self-supervised representation learning (SSL) in computer vision aims to leverage the
inherent structure and relationships within data to learn meaningful representations without …

Although supervised learning has been highly successful in improving the state-of-the-art in
the domain of image-based computer vision in the past, the margin of improvement has …

Self-supervised learning (SSL) is typically evaluated using a single metric (linear probing on
ImageNet), which neither provides insight into tradeoffs between models nor highlights how …

Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the
use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated …

Thyroid nodule classification and segmentation in ultrasound images are crucial for
computer-aided diagnosis; however, they face limitations owing to insufficient labeled data …

Recently, with the advancement of the Internet of Things (IoT), WiFi CSI-based HAR has
gained increasing attention from academic and industry communities. By integrating the …

Self-supervised learning (SSL) is a machine learning approach where the data itself
provides supervision, eliminating the need for external labels. The model is forced to learn …