Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Few-shot learning is an important and challenging research topic for remote sensing image
scene classification. Many existing approaches address this challenge by using meta …

Panoramic images with their 360deg directional view encompass exhaustive information
about the surrounding space, providing a rich foundation for scene understanding. To unfold …

The performance of nighttime semantic segmentation is restricted by the poor illumination
and a lack of pixel-wise annotation, which severely limit its application in autonomous …

We introduce the integrative task of few-shot classification and segmentation (FS-CS) that
aims to both classify and segment target objects in a query image when the target classes …

Through prompting, large-scale pre-trained models have become more expressive and
powerful, gaining significant attention in recent years. Though these big models have zero …

Few-shot classification aims to learn a discriminative feature representation to recognize
unseen classes with few labeled support samples. While most few-shot learning methods …

Abstract Recently, Vision Transformers have achieved impressive results on various Vision
tasks. Yet, their generalization ability under different distribution shifts is poorly understood …

Landslide mapping via pixel-wise classification of remote sensing imagery is essential for
hazard prevention and risk assessment. Deep-learning-based change detection greatly aids …

The ability of scene understanding has sparked active research for panoramic image
semantic segmentation. However, the performance is hampered by distortion of the …