center-based detection tracking center
[论文阅读] Anomaly Detection with Score Distribution Discrimination
Anomaly Detection with Score Distribution Discrimination 1 Introduction 如图1所示。Fig 1a~1c。这些方法基于学习到的输入数据的特征转换(如重构误差或embedding距离),生成异常分数。然而,在表示空间中的优化会导致数 ......
【UAV】ContextCapture Center介绍&安装包
ContextCapture Center是一款由ContextCapture公司开发的实景建模软件,它可将二维图像转化为精细的三维模型。ContextCapture Center具备强大的数据处理能力和高效的模型生成速度,使得用户能够在短时间内获得高质量的三维模型。 ......
【LINUX】ABRT has detected 1 problem(s). For more info run: abrt-cli list --since 1606480239
按照提示输入:abrt-cli list --since 1606480239 根据提示输入:abrt-auto-reporting enabled,退出后重新登录。 ......
pytorch分布式训练报错:Duplicate GPU detected : rank 1 and rank 0 both on CUDA device 35000
之前使用的比较老的torch 1.8.1,换到torch 2.0后报错 "rank 1 and rank 0 both on CUDA device 35000" 将main函数开头部分的初始化 ```python distributed.init_process_group(backend='nc ......
docker 打开报错 windows hypervisor is not present docker desktop is unable to detect a hypervisor. ..........
docker desktop - windows hypervisor is not present docker desktop is unable to detect a hypervisor. hardware assisted virtualization and data executio ......
git fatal detected dubious ownership in repository 的解决方法
我换了一台电脑,将旧电脑的硬盘换到新电脑上;我装了双系统,切换到另一个系统时;我发现了 git 代码仓库无法执行 git 命令,不断报错 fatal: detected dubious ownership in repository at 'C:\lindexi\Code\Foo' is owned ......
报错 Duplicate keys detected
参考:https://huaweicloud.csdn.net/638eab54dacf622b8df8cfd7.html?spm=1001.2101.3001.6661.1&utm_medium=distribute.pc_relevant_t0.none-task-blog-2~default~ ......
Bounce Tracking Mitigations All In One
Bounce Tracking Mitigations All In One
反弹跟踪缓解措施
......
[LeetCode] 1560. Most Visited Sector in a Circular Track
Given an integer n and an integer array rounds. We have a circular track which consists of n sectors labeled from 1 to n. A marathon will be held on t ......
【五期邹昱夫】CCF-A(TIFS'23)SAFELearning: Secure Aggregation in Federated Learning with Backdoor Detectability
> "Zhang, Zhuosheng, et al. "SAFELearning: Secure Aggregation in Federated Learning with Backdoor Detectability." IEEE Transactions on Information For ......
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection (2)
Feature backbone采用DLA,输入维度为3×H×W的RGB图,得到维度D×h×w的特征图F,然后将特征图送入几个轻量级regression heads,2D bouding boxes的中心特征图用下面的模块得到: 其中AN是Attentive Normalization.用公式表示: ......
论文阅读 《Pingmesh: A Large-Scale System for Data Center Network Latency Measurement and Analysis》
背景 在我们内部产品中,一直有关于网络性能数据监控需求,我们之前是直接使用 ping 命令收集结果,每台服务器去 ping (N-1) 台,也就是 N^2 的复杂度,稳定性和性能都存在一些问题,最近打算对这部分进行重写,在重新调研期间看到了 Pingmesh 这篇论文,Pingmesh 是微软用来监 ......
【V4下载】VOCALOID 4.5.2 V4 Plus Alpha编辑器下载/自带BPM Analyzer/Wave Track Transcoder/支持v5声库
【V4下载】VOCALOID 4.5.2 V4 Plus Alpha编辑器下载/自带BPM Analyzer/Wave Track Transcoder
支持V5 声库体质的Vocaloid 4!请支持正版!!! ......
Extract Abends with OGG-01028 Non-Standard Redo Detected in 10g Compatible Format
ogg 报错Extract Abends with OGG-01028 Non-Standard Redo Detected in 10g Compatible Format 抽取进程意外 Abend 手动重启恢复 Extract Abends with OGG-01028 Non-Standard ......
SAP GUI Scripting VBA Code Snippet to Detect all IDs of the UI Elements
'-Begin Option Explicit Dim gColl() As String Dim j As Integer Sub GetAll(Obj As Object) ' '- '- Recursively called sub routine to get the IDs of all ......
论文解读(MetaAdapt)《MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning》
Note:[ wechat:Y466551 | 可加勿骚扰,付费咨询 ] 论文信息 论文标题:MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning论文作者:Zhenrui Yue、Huimin Z ......
带你读论文丨S&P2019 HOLMES Real-time APT Detection
本文分享自华为云社区《[论文阅读] (09)S&P2019 HOLMES Real-time APT Detection(溯源图)》,作者: eastmount 。 摘要 本文提出了一种实现了检测高级持久性威胁(Advanced Persistent Threat,APT)新的方法,即HOLMES系 ......
critical error detected c0000374
记录一个堆栈被破坏的问题 debug 版本正常,release版本概率出现崩溃, release模式调试提示错误:critical error detected c0000374 问题不好跟,崩溃地方实际是没问题的,出问题的是在其他堆栈被破坏的地方 可能是:strcpy 拷贝字符串长度过长导致内存越 ......
[3d object detection] BEVFormer
**paper:** BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers, 2022 ## 1. Grid-shaped BEVquer ......
【Salesforce】【lwc】@api @track @wire
一、@api @track @wire的区别 1.@track注解private类型的reactive变量。 2. @api注解public类型的reactive变量(public类型:即可暴露给其他的APP用来可以赋值注入)。 3. @wire:我们常用的注解除了@track 以及 @api以外, ......
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection(1)
MonoCon的网络结构和MonoDLE几乎一样,只是添加了辅助学习(Auxiliary Learning, AL)模块. 网络结构如上图所示,对于3D目标检测来说,预测2D框是没有必要的,但是MonoCon在训练阶段仍然计算了2D框的损失函数,但是在推理的时候,并不会预测2D框,这就是所谓的辅助学 ......
Tracking Segments(二分,区间前缀)
#include <bits/stdc++.h> #define int long long using namespace std; const int N=1e6+10,mod=1e9+7; int n,t,a[N],f[N],res,num,ans,m,ll[N],rr[N],q,s[N]; ......
[LeetCode] 2013. Detect Squares
You are given a stream of points on the X-Y plane. Design an algorithm that: Adds new points from the stream into a data structure. Duplicate points a ......
通过docker安装的jira提示We've detected a potential problem with JIRA's Dashboard configuration that your administrator can correct. Click here to learn more
正常通过docker安装jira后,访问是不会出问题的 但是如果使用nginx代理后,就是在nginx里配置了proxy_pass http://localhost:2800 再访问后,就会报错We've detected a potential problem with JIRA's Dashbo ......
CPU环境下运行基于yolov5的行人检测代码(pedestrain detection based on yolov5 in CPU)
最近在捣腾基于 yolov5 的行人检测代码,在 github 上下载一个案例之后因为没用 GPU 运行一直碰壁,出现了许多 bug,现在整理了下 error 和解决方法,成功调试出了基于 yolov5 的行人检测代码,分享给大家~ 1. 运行环境:window10,CPU,Visual Studi ......
[论文笔记] Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit
作者受Faster-RCNN启发, 提出Line-CNN, 提出了一种新颖的车道线Anchor的表示方法,解决了车道线检测中表征的难点, 实现了端到端的车道线检测 ......
1843E - Tracking Segments
Problem - E - Codeforces 题意是现在有n个0,给你m段序列,然后给你q次操作,每次操作给一个x,把第x个0变成1,问你最少几次操作能出现一段序列里的1的数量大于0的数量,如果不存在,输出-1 对于操作数是一个递增序列。如果第k次操作后正好可行,那么就不用管k+1及以后了。 所 ......
ML-for-AGV-Dispatching:Center.py逐段解读
class Center(object): def __init__(self, env, x, y, routRule, AGV_num, WS_num, AGV_disRuleV, AGV_disRuleW, Ledispatch = "None", Task = ["None", "None" ......
Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis
# 根据实时流交互图分析技术的未知加密有害流量检测 ## 背景 ### 现有技术的不足 目前的加密流量检测大多基于根据已知攻击的先验知识的监督学习,对于未知类型的攻击难以检测 加密性: DPI检测和传统的基于签名的方法失效 多样性: 现有机器学习方法无法检测未知模式攻击,泛化能力差 ### 论文目的 ......