返回
Variance as a Guide: Vision Task-Adaptive Module Selection and Design
DOI:10.1016/j.eswa.2026.134318.png)
摘要
En 中文
• 视觉注意力保留全频信息,通过轻量级部分通道实现。
• 方差揭示不同视觉任务中的频率需求。
• 任务难度增加对高频视觉线索的需求。
Keyword:
Frequency Needs
Variance Analysis
Task-Adaptive Module Design
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
暂无机构信息
引用论文
Fusing global attention and self-attention for better performance and interpretability on tabular data融合全局注意力与自注意力,以提升表格数据上的性能与可解释性
Conv2Former: A Simple Transformer-Style ConvNet for Visual RecognitionConv2Former: 用于视觉识别的简单Transformer样式的ConvNet
Fetal Ultrasound Standard Plane Classification via Xception Enhancement and Brain Storm Optimization-Based Feature Selection基于Xception增强和基于Brain Storm Optimization的特征选择的胎儿超声标准平面分类
Hyperspectral image classification based on multi-scale equivariant feature extraction and geometric equivariant self-attention基于多尺度等变特征提取和几何等变自注意力的高光谱图像分类
Prediction of key parameters in aquaculture biofilters using a physics-informed spatial self-attention gated recurrent unit model基于物理信息空间自注意力门控循环单元模型的水产养殖生物滤器关键参数预测
MED-Net: Leveraging multi-visual encoding and Manhattan-distance pooling for robust medical image segmentationMED-Net: 利用多视觉编码和曼哈顿距离池化进行鲁棒医学图像分割

