The quadratic computation complexity of self-attention has been a persistent challenge
when applying Transformer models to vision tasks. Linear attention, on the other hand, offers …

Recent progress in vision Transformers exhibits great success in various tasks driven by the
new spatial modeling mechanism based on dot-product self-attention. In this paper, we …

The rapid advancement of remote sensing technology has significantly enhanced the
temporal resolution of remote sensing data. Multitemporal remote sensing image …

Transformer has shown excellent performance in remote sensing field with long-range
modeling capabilities. Remote sensing video (RSV) moving object detection and tracking …

Since the introduction of Vision Transformers, the landscape of many computer vision tasks
(eg, semantic segmentation), which has been overwhelmingly dominated by CNNs, recently …

Aiming at the problems of high missed detection rates of the YOLOv7 algorithm for vehicle
detection on urban roads, weak perception of small targets in perspective, and insufficient …

Self-attention mechanism has been a key factor in the recent progress of Vision Transformer
(ViT), which enables adaptive feature extraction from global contexts. However, existing self …

Diseases and insect pests of tea leaves cause huge economic losses to the tea industry
every year, so the accurate identification of them is significant. Convolutional neural …

Given sparse depths and the corresponding RGB images, depth completion aims at spatially
propagating the sparse measurements throughout the whole image to get a dense depth …

Conformer, combining convolution and self-attention sequentially to capture both local and
global information, has shown remarkable performance and is currently regarded as the …