526互联

数组for

Educational Codeforces Round 160 (Rated for Div. 2)

比赛录屏 \(A. Rating Increase\) https://codeforces.com/contest/1913/submission/237734923 \(B. Swap and Delete\) https://codeforces.com/contest/1913/submis ......
Educational Codeforces Round Rated 160

代码随想录算法训练营第五天| LeetCode242.有效的字母异位词、349. 两个数组的交集、202. 快乐数、1. 两数之和

LeetCode242.有效的字母异位词 ● 今日学习的文章链接和视频链接 代码随想录 (programmercarl.com) 题目链接 242. 有效的字母异位词 - 力扣（LeetCode） ● 自己看到题目的第一想法 public boolean anagram(String s, Stri ......
随想录 之和 训练营 数组 交集

LightGCL Simple Yet Effective Graph Contrastive Learning For Recommendation论文阅读笔记

Abstract 目前的图对比学习方法都存在一些问题，它们要么对用户-项目交互图执行随机增强，要么依赖于基于启发式的增强技术（例如用户聚类）来生成对比视图。这些方法都不能很好的保留内在的语义结构，而且很容易受到噪声扰动的影响。所以我们提出了一个图对比学习范式LightGCL来减轻基于CL的推荐者的通 ......
Recommendation Contrastive Effective LightGCL Learning

代码随想录算法训练营第六天|哈希表理论基础，242.有效的字母异位词，349. 两个数组的交集，202. 快乐数，1.两数之和

一、哈希表理论基础 学习： 1. 哈希法 当需要查询一个元素是否出现过，或者一个元素是否在集合里，首选哈希法 2. 实现哈希法的3种数据结构 数组：在哈希值个数比较小且范围可采用 集合：在哈希值个数或者范围较大时可采用 map：当既需要key，又要value时可采用 二、242.有效的字母异位词 题 ......
随想录 之和 训练营 数组 交集

Educational Codeforces Round 132 (Rated for Div. 2)

基本情况 AB秒了。C跨度有点太大，题解暂时都还没理解。 C. Recover an RBS Problem - C - Codeforces 待补题 ......
Educational Codeforces Round Rated 132

BigdataAIML-ML-Models for machine learning Explore the ideas behind machine learning models and some key algorithms used for each

最好的机器学习教程系列：https://developer.ibm.com/articles/cc-models-machine-learning/ By M. Tim Jones, Published December 4, 2017 Models for machine learning Alg ......
learning machine BigdataAIML-ML-Models BigdataAIML algorithms

Relation Networks for Object Detection

Relation Networks for Object Detection * Authors: [[Han Hu]], [[Jiayuan Gu]], [[Zheng Zhang]], [[Jifeng Dai]], [[Yichen Wei]] DOI: 10.1109/CVPR.2018.0 ......
Detection Relation Networks Object for

Deep Residual Learning for Image Recognition：ResNet

Deep Residual Learning for Image Recognition * Authors: [[Kaiming He]], [[Xiangyu Zhang]], [[Shaoqing Ren]], [[Jian Sun]] DOI: 10.1109/CVPR.2016.90 初读 ......
Recognition Residual Learning ResNet Image

Local Relation Networks for Image Recognition： LRNet

Local Relation Networks for Image Recognition * Authors: [[Han Hu]], [[Zheng Zhang]], [[Zhenda Xie]], [[Stephen Lin]] DOI: 10.1109/ICCV.2019.00356 @in ......
Recognition Relation Networks Local Image

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 ......
Convolutional Segmentation Rethinking Attention Semantic

CCNet: Criss-Cross Attention for Semantic Segmentation

CCNet: Criss-Cross Attention for Semantic Segmentation * Authors: [[Zilong Huang]], [[Xinggang Wang]], [[Yunchao Wei]], [[Lichao Huang]], [[Humphrey S ......
Segmentation Criss-Cross Attention Semantic CCNet

Dual Attention Network for Scene Segmentation：双线并行的注意力

Dual Attention Network for Scene Segmentation * Authors: [[Jun Fu]], [[Jing Liu]], [[Haijie Tian]], [[Yong Li]], [[Yongjun Bao]], [[Zhiwei Fang]], [[H ......
Segmentation 注意力 Attention Network Scene

Bottleneck Transformers for Visual Recognition

Bottleneck Transformers for Visual Recognition * Authors: [[Aravind Srinivas]], [[Tsung-Yi Lin]], [[Niki Parmar]], [[Jonathon Shlens]], [[Pieter Abbee ......
Transformers Recognition Bottleneck Visual for

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 ......
convolutional segmentation networks semantic Fully

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 ......
Convolutional Segmentation Biomedical Networks U-Net

SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation

SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation * Authors: [[Qiang Wan]], [[Zilong Huang]], [[Jiachen Lu]], [[Gang Yu]] ......
Squeeze-enhanced Segmentation Transformer SeaFormer Semantic

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]], [[ ......
High-Resolution Segmentation Multi-path Refinement Resolution

Expectation-Maximization Attention Networks for Semantic Segmentation 使用了EM算法的注意力

Expectation-Maximization Attention Networks for Semantic Segmentation * Authors: [[Xia Li]], [[Zhisheng Zhong]], [[Jianlong Wu]], [[Yibo Yang]], [[Zho ......
Expectation-Maximization Maximization Segmentation 算法 Expectation

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery * Authors: [[Libo Wang]], [[Rui Li]], [[ ......
segmentation transformer UNetFormer UNet-like efficient

Context Prior for Scene Segmentation带上下文先验的分割

Context Prior for Scene Segmentation * Authors: [[Changqian Yu]], [[Jingbo Wang]], [[Changxin Gao]], [[Gang Yu]], [[Chunhua Shen]], [[Nong Sang]] DOI: ......
先验 下文 Segmentation Context Prior

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

UNet++: A Nested U-Net Architecture for Medical Image Segmentation * Authors: [[Zongwei Zhou]], [[Md Mahfuzur Rahman Siddiquee]], [[Nima Tajbakhsh]], ......
Architecture Segmentation Medical Nested Image

Asymmetric Non-Local Neural Networks for Semantic Segmentation 非对称注意力

Asymmetric Non-Local Neural Networks for Semantic Segmentation * Authors: [[Zhen Zhu]], [[Mengdu Xu]], [[Song Bai]], [[Tengteng Huang]], [[Xiang Bai]] ......
Segmentation 注意力 Asymmetric Non-Local Networks

PSANet: Point-wise Spatial Attention Network for Scene Parsing双向注意力

PSANet: Point-wise Spatial Attention Network for Scene Parsing * Authors: [[Hengshuang Zhao]], [[Yi Zhang]], [[Shu Liu]], [[Jianping Shi]], [[Chen Cha ......
双向 注意力 Point-wise Attention Network

UIU-Net: U-Net in U-Net for Infrared Small Object Detection：Unet中的Unet

UIU-Net: U-Net in U-Net for Infrared Small Object Detection * Authors: [[Xin Wu]], [[Danfeng Hong]], [[Jocelyn Chanussot]] DOI: 10.1109/TIP.2022.32284 ......
Net U-Net Unet Detection Infrared

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 ......
Exemplar-Guided Class-Agnostic Scale-Prior Convolution Deformable

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 ......
Convolutional Enhancement Detection Adaptive Networks

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 ......
High-Resolution Segmentation 注意力 Deformable Resolution

探索 ECMAScript 2023 中的新数组方法

前言 ECMAScript 2023 引入了一些新功能，以改进语言并使其更加强大和无缝。这个新版本带来了令人兴奋的功能和新的 JavaScript 数组方法，使使用 JavaScript 编程更加愉快和轻松。本文将带领读者全面了解数组原型上新 JavaScript 方法。 什么是ECMAScript ......
数组 ECMAScript 方法 2023

349. 两个数组的交集

题目 349. 两个数组的交集 示例 1： 输入：nums1 = [1,2,2,1], nums2 = [2,2] 输出：[2] 示例 2： 输入：nums1 = [4,9,5], nums2 = [9,4,9,8,4] 输出：[9,4] 解释：[4,9] 也是可通过的 提示： 1 <= nums1 ......
数组 交集 两个 349

AWS - Required permissions for a node group role

Before you create worker nodes, you must create an IAM role with the following IAM policies: AmazonEKSWorkerNodePolicy AmazonEKS_CNI_Policy AmazonEC2C ......
permissions Required group node role
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