The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

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 …

Large-kernel convolutional neural networks (ConvNets) have recently received extensive
research attention but two unresolved and critical issues demand further investigation. 1) …

The Transformer network was originally proposed for natural language processing. Due to
its powerful representation ability for long-range dependency, it has been extended for …

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

Abstract Visual Attention Networks (VAN) with Large Kernel Attention (LKA) modules have
been shown to provide remarkable performance, that surpasses Vision Transformers (ViTs) …

We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by
recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few …

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 …

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

Owing to the success of transformer models, recent works study their applicability in 3D
medical segmentation tasks. Within the transformer models, the self-attention mechanism is …