Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …

In the past few years, the emergence of pre-training models has brought uni-modal fields
such as computer vision (CV) and natural language processing (NLP) to a new era …

The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …

Classification of land use and land cover from remote sensing images has been widely used
in natural resources and urban information management. The variability and complex …

Medical image segmentation has seen significant improvements with transformer models,
which excel in grasping far-reaching contexts and global contextual information. However …

Computer vision (CV) combined with a deep convolutional neural network (CNN) has
emerged as a reliable analytical method to effectively characterize and quantify high …

The farmers' welfare and its interlinkages to energy efficiency and farm sustainability has
attracted global scientific interest within the last few decades. This study examines the …

The task of semantic segmentation holds a fundamental position in the field of computer
vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent …

The evolving field of generative artificial intelligence (GenAI), particularly generative deep
learning, is revolutionizing a host of scientific and technological sectors. One of the pivotal …

This paper proposes a lightweight and efficient railway region extraction model LRseg,
which provides technical support for detecting foreign objects on the railway. LRseg consists …