Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

Attention has arguably become one of the most important concepts in the deep learning
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …

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

While originally designed for natural language processing tasks, the self-attention
mechanism has recently taken various computer vision areas by storm. However, the 2D …

Image segmentation groups pixels with different semantics, eg, category or instance
membership. Each choice of semantics defines a task. While only the semantics of each task …

Using light-weight architectures or reasoning on low-resolution images, recent methods
realize very fast scene parsing, even running at more than 100 FPS on a single GPU …

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …

As camera and LiDAR sensors capture complementary information in autonomous driving,
great efforts have been made to conduct semantic segmentation through multi-modality data …

Modern approaches typically formulate semantic segmentation as a per-pixel classification
task, while instance-level segmentation is handled with an alternative mask classification …

Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source
domain (eg synthetic data) to the target domain (eg real-world data) without requiring further …