super-resolution convolutional resolution real-time
Temporary failure in name resolution
tags: - error 虚拟机17导入旧镜像，ping baidu.com报错：Temporary failure in name resolution，但是ping ip，比如ping 8.8.8.8却正常。 修改/etc/resolv.conf，添加nameserver是不起作用的。 ubu ......
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
目录概符号说明MMGCN代码 Wei Y., Wang X., Nie L., He X., Hong R. and Chua T. MMGCN: Multi-modal graph convolution network for personalized recommendation of mic ......
Python hdfs 读取文件报错 Temporary failure in name resolution
问题背景 本人按照菜鸟教程的步骤，在windows系统布置了ubuntu虚拟机环境，并使用centos容器镜像搭建出单节点的hdfs服务。 欲使用Python hdfs api测试hdfs服务的功能，遂在ubuntu中编写以下代码准备测试 from hdfs import Client client ......
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation * Authors: [[Meng-Hao Guo]], [[Cheng-Ze Lu]], [[Qibin Hou]], [[Zhengning ......
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network * Authors: [[Wenzhe Shi]], [[Jose Caballer ......
Fully convolutional networks for semantic segmentation
Fully convolutional networks for semantic segmentation * Authors: [[Jonathan Long]], [[Evan Shelhamer]], [[Trevor Darrell]] DOI: 10.1109/CVPR.2015.729 ......
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation * Authors: [[Guosheng Lin]], [[Anton Milan]], [[Chunhua Shen]], [[ ......
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation * Authors: [[Olaf Ronneberger]], [[Philipp Fischer]], [[Thomas Brox]] Local library 初读 ......
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions 可变形卷积v3
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions * Authors: [[Wenhai Wang]], [[Jifeng Dai]], [[Zhe Chen]], [[Z ......
CBAM: Convolutional Block Attention Module
CBAM: Convolutional Block Attention Module * Authors: [[Sanghyun Woo]], [[Jongchan Park]], [[Joon-Young Lee]], [[In So Kweon]] doi:https://doi.org/10. ......
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers * Authors: [[Jiacong Xu]], [[Zixiang Xiong]], [[Shankar P. Bhattacharyya ......
A Deformable Attention Network for High-Resolution Remote Sensing Images Semantic Segmentation可变形注意力
A Deformable Attention Network for High-Resolution Remote Sensing Images Semantic Segmentation * Authors: [[Renxiang Zuo]], [[Guangyun Zhang]], [[Rong ......
Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting
Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting 初读印象 comment:: （计数用的一个网络）提出了一个标度优先的可变形卷积，将典范的信息，例如标度，整合到计数网络主干中。 动机 本文考 ......
Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images
Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images * Authors: [[Bowei Du]], [[Yecheng ......
2020CVPR_High-Resolution Image Synthesis with Latent Diffusion Models
1.AutoEncoder AutoEncoder（自编码器）是一种无监督学习的神经网络模型，用于学习有效的数据表示。它的目标是将输入数据编码成一种潜在的、紧凑的表示形式，然后从这个表示中重构原始输入。自编码器由两部分组成：编码器（Encoder）和解码器（Decoder）。编码器（Encoder） ......
论文精读：STMGCN利用时空多图卷积网络进行移动边缘计算驱动船舶轨迹预测（STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network）
《STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network》 论文链接：https://doi.org/10. ......
论文精读：基于具有时空感知的稀疏多图卷积混合网络的大数据驱动船舶轨迹预测（Big data driven trajectory prediction based on sparse multi-graph convolutional hybrid network withspatio-temporal awareness）
论文精读：基于具有时空感知的稀疏多图卷积混合网络的大数据驱动船舶轨迹预测 《Big data driven vessel trajectory prediction based on sparse multi-graph convolutional hybrid network with spati ......
2023ICCV_Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution
一. Motivation 1. transformer的工作主要集中在设计transformer块以获得全局信息，而忽略了合并高频先验的潜力 2. 关于频率对性能的影响的详细分析有限（Additionally, there is limited detailed analysis of the i ......
Exercise 3 - Convolutions
Exercise 3 - Convolutions 在视频中，您了解了如何使用卷积来改进时尚 MNIST。在练习中，请看您能否仅使用一个卷积层和一个 MaxPooling 2D 将 MNIST 的准确率提高到 99.8% 或更高。一旦准确率超过这一水平，就应停止训练。这应该在 20 个历元以内完成， ......
how convolutions work
how convolutions work 让我们在二维灰度图像上创建一个基本卷积，探索卷积是如何工作的。首先，我们可以从 scipy 中获取 "asccent "图像来加载图像。这是一张漂亮的内置图片，有很多角度和线条。 import cv2 import numpy as np from sci ......
Improving Computer Vision Accuracy using Convolutions
Improving Computer Vision Accuracy using Convolutions 在前面的课程中，你们了解了如何使用包含三层的深度神经网络（DNN）进行时装识别，这三层分别是输入层（数据的形状）、输出层（所需输出的形状）和隐藏层。你试验了不同大小的隐藏层、训练epoch ......
[ABC315Ex] Typical Convolution Problem
题目链接 首先观察到这个形式，容易发现它和常规的卷积不同点就在于：题目给出的求和定义中，\(\sum\) 符号下面的式子是 \(i+j<N\) 求和而不是 \(i+j=N\)。 为了方便计算，我们引入： \[G_n=\sum_{i+j<N}F_iF_j \]我们发现，假设所有 \(F_{1\sim{ ......
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
目录概符号说明MotivationChebNetII代码 He M., Wei Z. and Wen J. Convolutional neural networks on graphs with chebyshev approximation, revisited. NIPS, 2022. 概 作 ......
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
论文名： MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation "MS-TCN++: 用于动作分割的多阶段时域卷积" Shi-Jie Li#, Yazan AbuFarha#, Yun Liu, Mi ......
[论文阅读] Latent Consistency Models@ Synthesizing High-Resolution Images with Few-Step Inference
1. Pre title: Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference accepted: arXiv 2023 (ICLR 2024 Submission) paper ......
2023CVPR_Spatial-Frequency Mutual Learning for Face Super-Resolution
一. Network：SFMNet 1.网络采用U-Net结构，其中SFMLM-i是不同分辨率的每层结构 2.SPB是空域分支，FRB是频域分支，分别经过FRB和SPB的两个分支信息经过FSIB分支进行信息的融合 3. FRB结构： class FreBlock9(nn.Module): def _ ......
Distilling Knowledge from Graph Convolutional Networks
目录概符号说明DistillGCNLocal Structure Preserving代码 Yang Y., Qiu J., Song M., Tao D. and Wang X. Distilling knowledge from graph convolutional networks. CVP ......
Literature Survey about Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter
This is a literature survey about the paper of Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter. ......
《Generic Dynamic Graph Convolutional Network for traffic flow forecasting》阅读笔记
论文标题 《Generic Dynamic Graph Convolutional Network for traffic flow forecasting》 干什么活：交通流预测（traffic flow forecasting ） 方法：动态图卷积网络（Dynamic Graph Convolu ......