Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and
military domains. However, the high proportion of small objects in UAV images and the …

Abstract Recently lightweight Vision Transformers (ViTs) demonstrate superior performance
and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on …

In recent years, in the field of Multi-Agent Systems (MAS), significant progress has been
made in the research of algorithms that combine Reinforcement Learning (RL) with Attention …

Road defect detection is a crucial task for promptly repairing road damage and ensuring
road safety. Traditional manual detection methods are inefficient and costly. To overcome …

The attention module is the key component in Transformers. While the global attention
mechanism offers high expressiveness, its excessive computational cost restricts its …

Due to the limitations of small targets in remote sensing images, such as background noise,
poor information, and so on, the results of commonly used detection algorithms in small …

Abstract Vision Transformer (ViT) has gained increasing attention in the computer vision
community in recent years. However the core component of ViT Self-Attention lacks explicit …

Due to the depth degradation effect in residual connections many efficient Vision
Transformers models that rely on stacking layers for information exchange often fail to form …

Recent advances in contrastive language-image pretraining (CLIP) have demonstrated
strong capabilities in zero-shot classification by aligning visual and textual features at an …

Effective fusion of global and local multi-scale features is crucial for medical image
classification. Medical images have many noisy, scattered features, intra-class variations …