526互联

objectives you for and

Omkar and Akmar 题解

题意：有一个 \(n\) 个点的环，以及两个人。每个人可以向环中任意一个位置放置一个 \(A\) 或者 \(B\)，但是相邻的位置不能相同，不能行动者输。问最终的局面有多少种。 一个结论是：后手必胜。 证明：最终肯定不可能出现两个连续的空格，否则一定可以在其中一个上填 \(A\) 或 \(B\)。所 ......
题解 Omkar Akmar and

Animals and Puzzle 题解

原题链接：CF713D 题意：给定一个 \(n\times m\) 的地图 \(a\)，\(a_{i}\) 为 \(0\) 或 \(1\)。有 \(t\) 次询问，每次询问给定一个矩形，求出这个矩形中最大的由 \(1\) 构成的正方形的边长是多少。 首先考虑预处理出 \(d_{i,j}\) 表示以 ......
题解 Animals Puzzle and

B. Swap and Delete

原题链接 反思 要明确每个变量的含义！！！ 读题 1.取一对01置换，或者删掉一个元素，使得经过若干次改变后的序列\(t\)，和\(s\)的前\(|t|\)项元素各不相同。求问最少要删掉几个元素？ 一些事实的思考 1.对于一个给定的序列\(a\)，和另一个 “0的个数”与“1的个数”均相同，但是排列 ......
Delete Swap and

《CLIP：Connecting text and images》论文学习

一、Abstract 尽管深度学习已经彻底改革了计算机视觉领域，但当前的深度学习视觉方案方法存在几个主要问题： 高质量的视觉数据集，制作过程耗时且成本高昂，同时只包含了有限范围的视觉概念 标准的深度学习视觉模型（例如ImageNet、ResNet）擅长完成单一任务，且只能完成一个任务，需要投入巨大的 ......
Connecting images 论文 CLIP text

AWS EKS: Node Group Capacity and Auto Scaling Group Capacity

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Capacity Group Scaling Auto Node

Access denied for user 'root'@'%' to database 'information_schema'

原因 information_schema是一个虚拟的数据库，里面的表其实都是视图。应切换数据库为“真正的数据库” 解决 USE `THE-REAL-DATABASE`; ......
39 information_schema information database Access

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

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

BIgdataAIML-IBM-A neural networks deep dive - An introduction to neural networks and their programming

https://developer.ibm.com/articles/cc-cognitive-neural-networks-deep-dive/ By M. Tim Jones, Published July 23, 2017 Neural networks have been around f ......
networks neural BIgdataAIML-IBM-A introduction BIgdataAIML

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

Squeeze-and-Excitation Networks：SENet，早期cv中粗糙的注意力

Squeeze-and-Excitation Networks * Authors: [[Jie Hu]], [[Li Shen]], [[Samuel Albanie]], [[Gang Sun]], [[Enhua Wu]] Local library 初读印象 comment:: (SENet ......
Squeeze-and-Excitation 注意力 Excitation Networks Squeeze

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

Attention Is All You Need

Attention Is All You Need * Authors: [[Ashish Vaswani]], [[Noam Shazeer]], [[Niki Parmar]], [[Jakob Uszkoreit]], [[Llion Jones]], [[Aidan N. Gomez]], ......
Attention Need All You Is

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

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 ......
Super-Resolution Convolutional Resolution Real-Time Efficient

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

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

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
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