skeleton-based channel-wise convolution recognition
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
[TOC] > [Xia X., Yin H., Yu J., Wang Q., Cui L and Zhang X. Self-supervised hypergraph convolutional networks for session-based recommendation. AAAI, ......
Understanding Structural Vulnerability in Graph Convolutional Networks
Chen L., Li J., Peng Q., Liu Y., Zheng Z. and Yang C. Understanding structural vulnerability in graph convolutional networks. IJCAI, 2021. 概 mean 是在 G ......
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Tang J. and Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding. WSDM, 2018. 概 序列推荐的经典之作, 将卷积用在序列推荐之上. 符号说明 $\ma ......
Graph Convolutional Networks with EigenPooling
Ma Y., Wang S., Aggarwal C. C. and Tang J. Graph convolutional networks with eigenpooling. KDD, 2019. 概 本文提出了一种新的框架, 在前向的过程中, 可以逐步将相似的 nodes 和他们的特征聚合在 ......
An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale
模型如下图所示: 将H×W×C的图像reshape成了N×(P2×C),其中(H,W)是图像的原始分辨率,C是通道数,(P,P)是每个图像块的分辨率,N=H×W/P2为图像块的数量,将一个图像块使用可学习的线性层映射到维度为D的隐藏向量,如式(1)所示,线性映射的输出称为patch embeddin ......
阅读文献《DCRNet：Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems》
这篇文章的作者是广州大学的范立生老师和他的学生汤舜璞，于2022年10月发表在 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY。 文献提出了一种基于**空洞卷积（Dilated Convolution）**的CSI反馈网络，即空洞信道重建网络(Dilated Ch ......
Cluster-GCN An Efficient Algorithm for Training Deep Convolution Networks
Chiang W., Liu X., Si S., Li Y., Bengio S. and Hsieh C. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. ......
Spatiotemporal Remote Sensing Image Fusion Using Multiscale Two-Stream Convolutional Neural Networks
Spatiotemporal Remote Sensing Image Fusion Using Multiscale Two-Stream Convolutional Neural Networks abstract 地表反射率图像的渐变和突变是现有STF方法的主要挑战。(Gradual and ......
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Zou D., Hu Z., Wang Y., Jiang S., Sun Y. and Gu Q. Layer-dependent importance sampling for training deep and large graph convolutional networks. NIPS, ......
Multi-View Attribute Graph Convolution Networks for Clustering
论文阅读04-Multi-View Attribute Graph Convolution Networks for Clustering：MAGCN 论文信息 论文地址：Multi-View Attribute Graph Convolution Networks for Clustering | ......
FastGCN Fast Learning with Graph Convolutional Networks via Importance Sampling
Chen J., Ma T. and Xiao C. FastGCN: fast learning with graph convolutional networks via importance sampling. ICLR, 2018. 概 一般的 GCN 每层通常需要经过所有的结点的 prop ......
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Li Q., Han Z. and Wu X. Deeper insights into graph convolutional networks for semi-supervised learning. AAAI, 2018. 概 本文分析了 GCN 的实际上就是一种 Smoothing, 但是 ......
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Chen J., Zhu J. and Song L. Stochastic training of graph convolutional networks with variance reduction. ICML, 2018. 概 我们都知道, GCN 虽然形式简单, 但是对于结点个数非常多的 ......
Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation（阅读笔记）
空间信息引导卷积的实时RGBD语义分割（阅读笔记） 论文：Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation 复现：https://github.com/LinZhuoChen/SGNet（还 ......
卷积神经网络（Convolutional Neural Network）
前置芝士： 神经网络 #前言 人脑视觉机理，是指视觉系统的信息处理在可视皮层是分级的，大脑的工作过程是一个不断迭代、不断抽象的过程。视网膜在得到原始信息后，首先经由区域V1初步处理得到边缘和方向特征信息，其次经由区域V2的进一步抽象得到轮廓和形状特征信息，如此迭代地经由更多更高层的抽象最后得到更为精 ......
Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform
摘要 分析海洋声环境是一项棘手的任务。背景噪声和可变信道传输环境使舰船辐射噪声的准确识别变得复杂。现有的识别系统在处理多变的水下环境方面能力较弱，在实际应用中表现不佳。为了保持识别系统在各种水下环境下的鲁棒性，本文提出了一种自适应广义识别系统——AGNet (adaptive generalized ......
论文解读（CosFace）《CosFace: Large Margin Cosine Loss for Deep Face Recognition》
论文信息 论文标题：CosFace: Large Margin Cosine Loss for Deep Face Recognition论文作者：H. Wang, Yitong Wang, Zheng Zhou, Xing Ji, Zhifeng Li, Dihong Gong, Jin Zhou ......
python的人脸识别库face_recognition
代码： import cv2 import numpy as np import face_recognition img_train = face_recognition.load_image_file('query/1679370481783.jpg') img_train = cv2.cvtC ......
迁移学习（SOT）《Cross-domain Activity Recognition via Substructural Optimal Transport》
论文信息 论文标题：Cross-domain Activity Recognition via Substructural Optimal Transport论文作者：Wang Lu, Yiqiang Chen, Jindong Wang, Xin Qin论文来源：Neurocomputing论文地 ......
论文翻译：2022_DNS_1th：Multi-scale temporal frequency convolutional network with axial attention for speech enhancement
论文地址：带轴向注意的多尺度时域频率卷积网络语音增强 论文代码：https://github.com/echocatzh/MTFAA-Net 引用：Zhang G, Yu L, Wang C, et al. Multi-scale temporal frequency convolutional n ......