self-attention是一种特殊的注意力机制,起源于自然语言处理(NLP)。由于其具有较强的长程依赖性和适应性,在计算机视觉中发挥着越来越重要的作用。各种深度self-attention网络在不同视觉任务上的表现明显优于主流cnn,显示出基于注意力的模型的巨大潜力。然而,self-attention最初是最终为NLP设计的。在处理计算机视觉任务时...
针对第二个问题,重新审视LKA的关键属性,并发现局部信息与长距离依赖的相邻直接交互对于提供显著性能至关重要。因此,为了减轻LKA的复杂性,本文提出大坐标核注意力(Large Coordinate Kernel Attention, LCKA)模块,它将LKA中的深度卷积层(注意不是普通卷积)的2D卷积核分解为水平和垂直的1-D内核。 LCKA不仅能够在水平方...
In this paper, we propose a large-kernel attention block to enlarge the receptive field as well as maintain the details of thin branches. We reformulate the segmentation problem into pixel-wise segmentation and connectivity prediction with a differentiable connectivity modeling technique, and also ...
We introduce Large Kernel Attention (LKA) technology to decouple the large kernel convolutions. It can combine high accuracy with small computational cost. Furthermore, we use LKA as the basis for designing a new module (Res-VAN) that can be used to build backbone networks. This study ...
The integration of large kernel attention mechanisms within the convolutional layers enhances the model's capacity to capture fine-grained spatial details, thereby improving its predictive accuracy for meteorological phenomena. We introduce PuYun, comprising PuYun-Short for 0-5 day forecasts and PuYun-...
We have introduced a novel approach calledDeformable Large Kernel Attention (D-LKA Attention)to enhance medical image segmentation. This method efficiently captures volumetric context using large convolution kernels, avoiding excessive computational demands. D-LKA Attention also benefits from deformable convolu...
Kin Wai Lau, Lai-Man Po, Yasar Abbas Ur Rehman,Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN [arXiv paper] The implementation code is based on theVisual Attention Network (VAN), Computational Visual Media, 2023. For more information, please refer to the...
It should be noted that both the attention mechanism and Transformer utilize the concept of attention to enhance the representational capability of the model. When classifying models, those based on the attention mechanism and those that combine CNN with the attention mechanism are categorized as ...
为了解决这些挑战,我们引入了Deformable Large Kernel Attention (D-LKA Attention)} 的概念,这是一种采用大卷积核来充分理解体素上下文的简化注意力机制。 这种机制在类似于自注意力的感受野中运行,同时避免了计算开销。 此外,我们提出的注意力机制受益于可变形卷积来灵活地扭曲采样网格,使模型能够适当地适应不同的数据...
提出了D-LKA Attention,这是一种高效的注意力机制,使用大卷积核来充分理解体积上下文,同时避免了计算开销。 引入了可变形卷积,使模型能够适应不同的数据模式,更好地捕捉医学图像中的变形。 设计了2D和3D版本的D-LKA Net架构,后者在跨深度数据理解方面表现出色。 在多个流行的医学分割数据集上(如Synapse、NIH Pancr...