Semantic segmentation has a broad range of applications in a variety of domains including
land coverage analysis, autonomous driving, and medical image analysis. Convolutional …

Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …

The success of the Segment Anything Model (SAM) demonstrates the significance of data-
centric machine learning. However, due to the difficulties and high costs associated with …

Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover map**, urban change detection …

Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the
growing size of very-high-resolution (VHR) remote sensing images poses challenges in …

This article presents a transformer and convolutional neural network (CNN) hybrid deep
neural network for semantic segmentation of very high resolution (VHR) remote sensing …

In this paper, we consider enhancing medical visual-language pre-training (VLP) with
domain-specific knowledge, by exploiting the paired image-text reports from the radiological …

Accurate semantic segmentation of remote sensing data plays a crucial role in the success
of geoscience research and applications. Recently, multimodal fusion-based segmentation …

Accurate segmentation of organs or lesions from medical images is crucial for reliable
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …