Deep models trained on source domain lack generalization when evaluated on unseen
target domains with different data distributions. The problem becomes even more …

Driving in adverse weather conditions is a key challenge for autonomous vehicles (AV).
Typical scene perception models perform poorly in rainy, foggy, snowy, and cloudy …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Even though convolutional neural networks (CNNs) are driving progress in medical image
segmentation, standard models still have some drawbacks. First, the use of multi-scale …

In this article, we propose a Dual Relation-aware Attention Network (DRANet) to handle the
task of scene segmentation. How to efficiently exploit context is essential for pixel-level …

Semantic segmentation models gain robustness against poor lighting conditions by virtue of
complementary information from visible (RGB) and thermal images. Despite its importance …

The term glaucoma refers to a group of heterogeneous diseases that cause the
degeneration of retinal ganglion cells (RGCs). The degeneration of RGCs leads to two main …

Recent semantic segmentation methods exploit encoder-decoder architectures to produce
the desired pixel-wise segmentation prediction. The last layer of the decoders is typically a …

Incorporating the depth (D) information to RGB images has proven the effectiveness and
robustness in semantic segmentation. However, the fusion between them is not trivial due to …

Emergence of large datasets and resilience of convolutional models have enabled
successful training of very large semantic segmentation models. However, high capacity …