Semantic segmentation of remote sensing images has received increasing attention in
recent years; however, using a single imaging modality limits the segmentation …

Vision Transformer models process input images by dividing them into a spatially regular
grid of equal-size patches. Conversely, Transformers were originally introduced over natural …

Although networks with traditional convolutional neural network (CNN) backbones have
achieved remarkable results in red-green-blue–depth (RGB-D) semantic segmentation …

Scene parsing of high spatial resolution (HSR) remote sensing images has achieved
notable progress in recent years by the adoption of convolutional neural networks. However …

Real-time semantic segmentation is in intense demand for the application of autonomous
driving. Most of the semantic segmentation models tend to use large feature maps and …

Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is
a popular vision-based approach. While deep neural networks have shown impressive …

Path guiding is a promising technique to reduce the variance of path tracing. Although
existing online path guiding algorithms can eventually learn good sampling distributions …

We present a novel pyramidal output representation to ensure parsimony with our"
specialize and fuse" process for semantic segmentation. A pyramidal" output" representation …

Indoor semantic segmentation is a long-standing vision task that has been recently
advanced by convolutional neural networks (CNNs), but this task remains challenging by …

Self-supervised monocular depth prediction is a widely researched field that aims to provide
a better scene understanding. However, most existing methods prioritize prediction accuracy …