To address this, this paper proposes a model based on Dynamic Multi-Scale Convolution for group recognition of familiar and unfamiliar faces. This approach uses generated weight masks for cross-subject familiar/unfamiliar face recognition using a multi-scale model. The model employs a variable-length...
We use a set of learnable convolutions as an encoder to downsample the input moiré image to 12, 14 and 18 of the original resolution, encoding image features in different frequency bands. Two key parts in our proposed method, multi-scale structure and dynamic feature encoding will be ...
提出使用“multi-scale”CNN对图像去模糊,采用“端对端”(end-to-end)的方式,即输入一张模糊图像,网络将输出一张同等大小的清晰图像;提出“multi-scale”损失函数,用于模拟传统的“coarse-to-fine”的去模糊方法 提出较大规模的GOPRO数据集,一共由3214对模糊-清晰图像构成Introduction...
这篇文章提出了一种基于多尺度图卷积网络的3D人体运动预测方法——dynamic multiscale graph neural network (DMGNN)。该网络结构分为编码器和解码器,其中编码器由一系列多尺度图计算单元(multiscale graph computational unit,MGCU)组成,解码器由图时序门单元(graph-based GRU)组成。该方法通过MGCU提取不同尺度的人...
动态卷积(Dynamic Convolution)是《DynamicConv.pdf》中提出的一种关键技术,旨在增加网络的参数量而几乎不增加额外的浮点运算(FLOPs)。以下是关于动态卷积的主要信息和原理: 主要原理: 1. 动态卷积的定义: 动态卷积通过对每个输入样本动态选择或组合不同的卷积核(称为"experts"),来处理输入数据。这种方法可以视为是对...
To interact with local and global features, we combine this module with graph convolution to design a multi-scale dynamic graph convolutional network (MDGNet). In summary, the contributions made in this paper are as follows: (1) Based on the characteristics of DR image lesion features, we...
Multi-scale feature fusion module Because the size of different tools in the assembly behavior image is inconsistent, this has a large impact on the recognition of assembly behaviors. Meanwhile, if the convolutional neural network uses convolution kernels of the same size to extract the features of...
By constructing the convolution wavefield objective function, the source difference between simulated and observed data is ignored, thus avoiding the source wavelet estimation. Theoretically, this process has no restriction on the accuracy of the wavelet, and a multi-scale inversion strategy can be ...
本文将介绍一种基于动态卷积网络(Dynamic Convolutional Networks)、多尺度特征融合网络(Multi-scale Feature Fusion Networks)和自适应损失函数(Adaptive Loss Functions)的智能图像分类模型,采用了PyTorch框架进行实现,并通过PyQt构建了简洁的用户图像分类界面。该模型能够处理多分类任务,并且提供了良好的可扩展性和轻量化...
Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122. https://doi.org/10.48550/arXiv.1511.07122 Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of th...