Deep learning-based intelligent vehicle perception has been developing prominently in
recent years to provide a reliable source for motion planning and decision making in …

With many platforms and sensors continuously observing the earth surface, the large
amount of remote sensing data presents a big data challenge. While remote sensing data …

Remote sensing (RS) image scene classification has obtained increasing attention for its
broad application prospects. Conventional fully-supervised approaches usually require a …

The domain adaptation (DA) approaches available to date are usually not well suited for
practical DA scenarios of remote sensing image classification since these methods (such as …

This work investigates unsupervised domain adaptation (UDA)-based semantic
segmentation of very high-resolution (VHR) remote sensing (RS) images from different …

Semantic segmentation is a key technique involved in automatic interpretation of high-
resolution remote sensing (HRS) imagery and has drawn much attention in the remote …

Unsupervised domain adaptation (UDA) is an important solution to reduce the bias between
the labeled source domain and the unlabeled target domain. It has attracted more attention …

Deep learning has shown promising performance in automated sewer defect detection,
however, is generally data-driven and computationally intensive. Transfer learning (TL) …

In recent years, the success of large-scale vision-language models (VLMs) such as CLIP
has led to their increased usage in various computer vision tasks. These models enable …

Since last decade, deep learning has made exceptional progress in various fields of artificial
intelligence including image and voice recognition, natural language processing. Inspired …