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 …

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Driven by improved architectures and better representation learning frameworks, the field of
visual recognition has enjoyed rapid modernization and performance boost in the early …

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 …

Remarkable effectiveness of the channel or spatial attention mechanisms for producing
more discernible feature representation are illustrated in various computer vision tasks …

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 …

Camouflaged object detection (COD) aims to address the tough issue of identifying
camouflaged objects visually blended into the surrounding backgrounds. COD is a …

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 …