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 Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …

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

In this work, we introduce Dual Attention Vision Transformers (DaViT), a simple yet effective
vision transformer architecture that is able to capture global context while maintaining …

Most recent semantic segmentation methods adopt a U-Net framework with an encoder-
decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to …

Vehicle detection technology is of great significance for realizing automatic monitoring and
AI-assisted driving systems. The state-of-the-art object detection method, namely, a class of …

Learning a single static convolutional kernel in each convolutional layer is the common
training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent …

Recently, it has been demonstrated that the performance of a deep convolutional neural
network can be effectively improved by embedding an attention module into it. In this work, a …

Stocking density presents a key factor affecting livestock and poultry production on a large
scale as well as animal welfare. However, the current manual counting method used in the …

This paper reviews the third biennial challenge on spectral reconstruction from RGB images,
ie, the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB …