Single Domain Generalization for Crowd Counting Zhuoxuan Peng, Gary S.-H. Chan(本名暴露,不过也无所谓了╮(╯▽╰)╭) [arXiv] [Github] 引言 进入正题,这篇文章研究的是人群计数的单领域泛化问题。人群计数(crowd counting)是个经典的计算机视觉问题,简单点说就是数出图片中含有多少人。主流方法的做法是估...
与之前Crowd Counting中Domain Adaptive方法的不同:不依赖于未知场景的数据进行训练 本文的主要贡献: 我们引入了第一个domain-general人群计数框架,该框架在一个源域(source domain)上训练,可以很好地推广到任何未知的目标域。 我们设计了领域不变(DICM)和领域特定(DSCM)的群体记忆模块,从图像特征中的领域特定信息中分...
Domain shift across crowd data severely hinders crowd counting models to generalize to unseen scenarios. Although domain adaptive crowd counting approaches close this gap to a certain extent, they are still dependent on the target domain data to adapt (e.g. finetune) their models to the specific...
A CNN-based crowd counting approach which utilizes the first 13 layers of pre-trained VGG-16 model and dilated convolutional layers to generate quality density maps is proposed. The dilated layers allow for larger receptive fields without increasing the amount of computation. In addition, a ...
Recent deep networks have convincingly demonstrated high capability in crowd counting, which is a critical task attracting widespread attention due to its various industrial applications. Despite such progress, trained data-dependent models usually can not generalize well to unseen scenarios because of the...
model.py add implementation May 4, 2022 README This repository is a PyTorch implementation for the paperLeveraging Self-Supervision for Cross-Domain Crowd Counting, which is accepted asoralpresentation at CVPR 2022. If you use this code in your research, please citethe paper. ...
Issues1 Pull requests Actions Projects Security Insights Additional navigation options master 2Branches 0Tags Code README NLT - Cross-domain Crowd Counting This repo is the official implementation ofpaper:Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting (T-NNLS, 2021). The ...
With things like crowd funding and with like the Lean Startup taking hold, I can just see more people that still want an (Unclear 54:25.2) name, but they do not want to put all this money into it. And on the flipside, with domain parking being where it is, I definitely want the ...
主要思路和创新点 经典的跨域算法通常会对源域进行处理:1. style transfer (已知目标域的风格,只使用了目标域先验); 2. Domian Randomization(不知目标域风格,增强模型跨域表达能力,没有使用目标先验)。这些转换方式不是最优的,因此作者提出了基于任务和目标风格的自动搜索转换器。具体做法如下所示:每获得一个转换...
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