ODConv 利用一种新颖的多维注意力机制和并行策略,在任何卷积层的卷积核空间的所有四个维度上学习卷积核的互补注意力。作为常规卷积的替代品,ODConv 可以插入许多 CNN 架构中。在 ImageNet 和 MS-COCO 数据集上的广泛实验表明,ODConv 为各种主流的 CNN 骨干网络带来了稳固的准确性提升,包括轻量级和大型网络,例如,在 ...
ODConv 利用一种新颖的多维注意力机制和并行策略,在任何卷积层的卷积核空间的所有四个维度上学习卷积核的互补注意力。作为常规卷积的替代品,ODConv 可以插入许多 CNN 架构中。在 ImageNet 和 MS-COCO 数据集上的广泛实验表明,ODConv 为各种主流的 CNN 骨干网络带来了稳固的准确性提升,包括轻量级和大型网络,例如,在 ...
代码:github.com/OSVAI/ODConv 摘要 在每个卷积层中学习单个静态卷积核[1]是现代卷积神经网络(CNN)的常见训练范式。相反,最近对动态卷积的研究表明,学习n个卷积核的线性组合,并对其输入相关注意进行加权,可以显著提高轻量级CNN的精度,同时保持有效的推理。然而,我们观察到,现有的工作通过核空间的一维(关于卷积核数)赋...
Omni-Dimensional Dynamic Convolutionopenreview.net/forum?id=DmpCfq6Mg39 代码地址: https://github.com/OSVAI/ODConv(未更新)github.com/OSVAI/ODConv 摘要 对于每个卷积层,学习一个静态的卷积是卷积神经网络通用的做法,近几年也有对动态卷积进行研究,学习N个卷积核的选型组合,并对其进行注意力加权,但这...
Therefore, an X-ray omni-dimensional dynamic convolution feature-coordinate attention (CA) network named X-ODFCANet is proposed. By adding the FCA and ODConv modules to the residual network, the ability of the network to extract feature information from chest radiographs is enhanced. A diagram of...
This network incorporates a feature coordination attention module and an omni-dimensional dynamic convolution (ODConv) module, leveraging the residual module for feature extraction from X-ray images. The feature coordination attention module utilizes two one-dimensional feature encoding processes to aggregate...
This repository is an official PyTorch implementation of"Omni-Dimensional Dynamic Convolution", ODConv for short, published by ICLR 2022 as a spotlight. ODConv is a more generalized yet elegant dynamic convolution design, which leverages a novel multi-dimensional attention mechanism with a parallel stra...
This network incorporates a feature coordination attention module and an omni-dimensional dynamic convolution (ODConv) module, leveraging the residual module for feature extraction from X-ray images. The feature coordination attention module utilizes two one-dimensional feature encoding processes to aggregate...
This paper proposes a bridge defect detection scheme YOLOv5 based on multi-softmax and omni-dimensional dynamic convolution (MOD-YOLO), which combines the proposed multi-softmax classification loss function with omni-dimensional dynamic convolution (ODConv). MOD-YOLO is evaluated on codebrim dataset ...
Omni-Dimensional Dynamic Convolution By Chao Li, Aojun Zhou and Anbang Yao. This repository is an official PyTorch implementation of "Omni-Dimensional Dynamic Convolution", ODConv for short, published by ICLR 2022 as a spotlight. ODConv is a more generalized yet elegant dynamic convolution design,...