Semantic segmentation, also called scene labeling, refers to the process of assigning a
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …

We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …

Prevalent semantic segmentation solutions, despite their different network designs (FCN
based or attention based) and mask decoding strategies (parametric softmax based or pixel …

We present SegFormer, a simple, efficient yet powerful semantic segmentation framework
which unifies Transformers with lightweight multilayer perceptron (MLP) decoders …

Abstract Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level
performance in edge detection with the rich and abstract edge representation capacities …

Crack detection is of significant importance for concrete structural inspection. Unmanned
aerial vehicle (UAV)-based crack detection systems abound, but simply quantifying cracks …

In recent years, convolutional neural networks (CNNs) are leading the way in many
computer vision tasks, such as image classification, object detection, and face recognition. In …

Scene understanding of high resolution aerial images is of great importance for the task of
automated monitoring in various remote sensing applications. Due to the large within-class …

Current state-of-the-art methods for image segmentation form a dense image representation
where the color, shape and texture information are all processed together inside a deep …

In recent years semantic segmentation has become a pivotal tool in processing and
interpreting satellite imagery. Yet a prevalent limitation of supervised learning techniques …