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 …

Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …

Learning-based infrared small object detection methods currently rely heavily on the
classification backbone network. This tends to result in tiny object loss and feature …

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

Convolution and self-attention are two powerful techniques for representation learning, and
they are usually considered as two peer approaches that are distinct from each other. In this …

Vision transformers have been successfully applied to image recognition tasks due to their
ability to capture long-range dependencies within an image. However, there are still gaps in …

A variety of attention mechanisms have been studied to improve the performance of various
computer vision tasks. However, the prior methods overlooked the significance of retaining …

Recent studies on mobile network design have demonstrated the remarkable effectiveness
of channel attention (eg, the Squeeze-and-Excitation attention) for lifting model performance …