论文链接: 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...
3D稀疏卷积粗略理解:Submanifold Sparse Convolution和Spatially Sparse Convolution以及SECOND网络理解 1.分类对于稀疏卷积有两种: 一种是Spatially Sparse Convolution ,在spconv中为 SparseConv3d。就像普通的卷积一样,只要kernel 覆盖一个 active input site,就可以计算出output site。… 冰锐 [基础知识整理] 空洞卷积(...
【YOLOv8改进】 ODConv(Omni-Dimensional Dynamic Convolution):全维度动态卷积 摘要 在现代卷积神经网络(CNN)中,每个卷积层中学习单个静态卷积核是常见的训练范式。然而,最近在动态卷积的研究中表明,通过学习 n 个卷积核的线性组合,并且这些卷积核的权重取决于它们的输入相关注意力,可以显著提高轻量级 CNN 的准确性,...
简介:ODConv是一种增强型动态卷积方法,通过多维注意力机制在卷积的四个维度上学习互补注意力,提升轻量级CNN准确性和效率。与现有动态卷积不同,ODConv覆盖了空间、输入/输出通道和核数维度。在ImageNet和MS-COCO上,对MobileNetV2|ResNet等模型有显著性能提升,减少参数的同时超越传统方法。代码和论文链接可用。在YOLO系列...
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...
Correction: Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification Y Li,Y Xin,X Li,... - 《Visual Computing for Industry Biomedicine & Art》 被引量: 0发表: 2024年 MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional ...
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 st...
et al. Correction: Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification. Vis. Comput. Ind. Biomed. Art 7, 19 (2024). https://doi.org/10.1186/s42492-024-00170-x Download citation Published23 July 2024 DOIhttps://doi.org/10.1186/s42492-024-00170...
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 ...
The official project website of "Omni-Dimensional Dynamic Convolution" (ODConv for short, spotlight in ICLR 2022). - GitHub - OSVAI/ODConv: The official project website of "Omni-Dimensional Dynamic Convolution" (ODConv for short, spotlight in ICLR 2022)