在论文【Time-aware Large Kernel Convolutions】中,提出了一种新的自适应卷积操作,学习预测求和核的大小,而不是使用固定大小的核矩阵,称为TaLK 卷积。该方法的时间复杂度为O(n),能够有效地使序列编码过程与标记的数量成线性关系。在大规模标准机器翻译、抽象摘要和语言建模数据集上评估了所提出的方法,并表明TaLK ...
在本文中提出了一种新的Large Kernel Attention(LKA)模块,以使self-attention的自适应和长距离相关,同时避免了上述问题。作者进一步介绍了一种基于LKA的新的神经网络,即视觉注意力网络(VAN)。VAN非常简单和高效,并在图像分类、目标检测、语义分割、实例分割等大量实验方面,它的性能优于最先进的Vision Transformer和卷积...
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 ...
Although Large Kernel Convolution has been widely used in the field of computer vision, its potential in land cover change detection of remote sensing images has not been fully explored. To address this, a novel Re-parameterization Large kernel Convolution Network for Change Detection (CD-RLKNet)...
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...
We designed a new convolutional module, DConv, by incorporating the dynamic large convolution kernel (DLK) and dynamic feature fusion (DFF) modules from ... H Ma,L Bai,Y Li,... - International Conference on Intelligent Computing 被引量: 0发表: 2024年 Joint attention mechanism with dynamic ...
LKM-UNet: Large Kernel Vision Mamba for Medical Segmentation elevates SSMs beyond Convolution and Self-attention 🚀Large Kernel Vision Mamba UNet for Medical Image Segmentation Requirements:python 3.10 + torch 2.0.1 + torchvision 0.15.2 (cuda 11.8)...
Although large kernel convolutions have received atten- tion in general object recognition, there has been a lack of research examining their significance in remote sensing de- tection. As previously noted in Sec. 1, aerial images possess unique characteristics that make la...
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。
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation DCNv4 Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications DAS DAS: A Deformable Attention to Capture Salient Information in CNNs D3Dnet Deformable 3D Convolution for Video Super...