segmentation information convolution real-time
An improved LSTM-based model for identifying high working intensity load segments of the tractor load spectrum
一区top Computers and Electronics in Agriculture 题目: “基于改进 lstm 的拖拉机载荷谱高工作强度载荷段识别模型” (pdf) “An improved LSTM-based model for identifying high working in ......
基于融合语义信息改进的内容推荐算法。Improved content recommendation algorithm integrating semantic information.
引言 路漫漫其修远兮,吾将上下而求索。每天一篇论文,做更好的自己。 本文读的这篇论文为发表于2023年5月28日的一篇名为《基于融合语义信息改进的内容推荐算法》(基于融合语义信息改进的内容推荐算法)的文章,文章主要介绍了基于内容的推荐技术在电子商务和教育领域的广泛应用,以及传统基于内容推荐技术在语义 ......
Learning Dynamic Query Combinations for Transformer-based Object** Detection and Segmentation论文阅读笔记
Motivation & Intro 基于DETR的目标检测范式(语义分割的Maskformer也与之相似)通常会用到一系列固定的query,这些query是图像中目标对象位置和语义的全局先验。如果能够根据图像的语义信息调整query,就可以捕捉特定场景中物体位置和类别的分布。例如,当高级语义显示图 ......
1.9 Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation 基于语义分割遥感图像的模型
Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation 参考遥感图像分割的旋转多尺度交互网络 参考遥感图像分割 (RRSIS)是一个新的挑战,它结合了计算机视觉和自然语言处理,通过 ......
5 Anonymous Informant
题目大致就是让你判断有没有一个a数组,选一个定点向左循环x次,这样的操作一个k次,能变成给定的b数组 其实这样的题目是死的,你要做的只不过是不断的倒推。 当你找不到一个可以操作的定点,说明是不行。 因为k很大不可以循环1e9次的,说明这个题目一定要缩小k的范围。这其中有一个思想就是如果模拟到了同一个 ......
Segment Anything(SAM)环境安装&代码调试
引子 Segment Anything是前阵子大火的CV领域模型,之前也有尝试,只是没有整理。OK,让我们开始吧 一、拉取下载docker镜像 docker pull cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04 二、安装SAM环境 do ......
信息与通信技术(ICT,information and communications technology)
ICT人,你真的知道什么是ICT嘛? 一树网络实验室 关注她 8 人赞同了该文章 随着各行各业的信息化技术发展及应用,IT、OT、CT这三个原本相互独立发展的技术体系开始实现紧密融合,新的技术行业体系整合形成ICT行业。 CT(Communication Technology) CT指通信技术(C ......
A Long read hybrid error correction algorithm based on segmented pHMM
A Long read hybrid error correction algorithm based on segmented pHMM 2023/12/15 11:06:36 The "Long read hybrid error correction algorithm based on se ......
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 ......
【模板】李超线段树 / [HEOI2013] Segment
李超线段树是一种用于维护平面直角坐标系内线段关系的数据结构,插入直线/线段,支持查询单点极值 李超树的经典应用是斜率优化,可以看下这篇文章 李超线段树没有用懒标记实现区间修改,而用的是标记永久化 其实标记永久化与我们对lazy标记的理解非常相同,可以看看LYD蓝书上对标记永久化的解释,都是累积某个节 ......
Only the invariant culture is supported in globalization-invariant mode. See https://aka.ms/GlobalizationInvariantMode for more information
错误信息:全球化不变模式只支持不变文化。看见https://aka.ms/GlobalizationInvariantMode了解更多信息 修改引用配置即可:<InvariantGlobalization>true</InvariantGlobalization> 改为false Only the ......
MySql的information_schema.processlist库学习之"如何检测出大数据sql查询"
1.如何通过MySql检测出大数据sql查询 一般数据库都会存在:information_schema数据库 检测出大数据sql查询[time时间越长说明,数据量越大,要根据公司的限度来衡量,我的思路是500以上都要查看是否是大数据的范畴] 2.案例 -- 检测出大数据sql查询[time时间越长说 ......
A Long read hybrid error correction algorithm based on segmented pHMM 基于pHMM的DNA序列分析与错误修正方法研究
基于pHMM的DNA序列分析与错误修正方法研究 这篇论文主要内容是关于DNA序列分析中的错误纠正方法。论文提出了一种基于概率隐马尔可夫模型(pHMM)的错误纠正方法。首先,通过SR-LR对齐和基于短读序列对齐的预处理步骤,对DNA序列进行处理。然后,利用pHMM构建了一个隐藏的马尔可夫模型,并进行前 ......
A. Anonymous Informant
原题链接 前言 一道精简但是内容丰富的题 一些事实 1.循环左移len位后数组的节点对应原数组的节点,相当于在无限自复制循环的数组中将原来的节点右移len位 2.如果该数组能被定点数组循环左移x位得到,那么该数组最后一个节点的值一定是x 3.不管怎么位移,可能的数组最多只有n种不同的情况(1~n分别 ......
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`; ......
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 ......
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 ......
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 ......
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 ......
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 ......
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 ......
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 ......
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]] ......
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]], [[ ......
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 ......
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]], [[ ......
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 ......
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. ......
SegViT: Semantic Segmentation with Plain Vision Transformers
SegViT: Semantic Segmentation with Plain Vision Transformers * Authors: [[Bowen Zhang]], [[Zhi Tian]], [[Quan Tang]], [[Xiangxiang Chu]], [[Xiaolin We ......
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: ......