Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …

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

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and
propose the SegViT. Previous ViT-based segmentation networks usually learn a pixel-level …

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with
an encoder-decoder architecture. The encoder progressively reduces the spatial resolution …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Point clouds can be represented in many forms (views), typically, point-based sets, voxel-
based cells or range-based images (ie, panoramic view). The point-based view is …

Convolution exploits locality for efficiency at a cost of missing long range context. Self-
attention has been adopted to augment CNNs with non-local interactions. Recent works …

In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic
segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve …