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...
Single Domain Generalization forCrowd 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计...
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...
In order to solve the problem of weak single domain generalization ability in existing crowd counting methods, this study proposes a new crowd counting framework called Multi-scale Attention and Hierarchy level Enhancement (MAHE). Firstly, the model can focus on both the detailed features and the ...
This is an official repository for our CVPR2024 work, "Single Domain Generalization for Crowd Counting". You can read our paperhere. Requirements Python 3.10.12 PyTorch 2.0.1 Torchvision 0.15.2 Others specified inrequirements.txt Data Preparation ...
内容提示: Leveraging Self-Supervision for Cross-Domain Crowd CountingWeizhe Liu Nikita Durasov Pascal FuaComputer Vision Laboratory,´Ecole Polytechnique Fédérale de Lausanne (EPFL){weizhe.liu, nikita.durasov, pascal.fua}@epfl.chAbstractState-of-the-art methods for counting people in crowdedscenes...
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from being deployed in emergencies during which...
来自专栏 · Crowd Counting 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...
Paper tables with annotated results for Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting
This paper investigates crowd counting in the frequency domain, which is a novel direction compared to the traditional view in the spatial domain. By transforming the density map into the frequency domain and using the nice properties of the characteristic function, we propose a novel method that ...