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...
[2212.02573] Domain-General Crowd Counting in Unseen Scenarios (arxiv.org) 代码链接: ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios (github.com) 编辑于 2024-04-08 11:41・IP 属地湖北 人群计数 AAAI2023 计算机视觉 赞同66 条评论 分享喜欢收藏申请转载 ...
Image quality assessment for closed-loop computer-assisted lung ultrasound 34 p. An Approximation of Forward Self-Similar solutions to the 3D Navier-Stokes system 18 p. Exclusive $B^0 to pi^- ell^+ nu_ell$ Decays with Hadronic Full Event Interpretation Tagging in 34.6 fb$^{-1}$ of Be...
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...
2.1. Domain Adaptive Crowd Counting There are two groups of domain adaptation works in crowd counting: real-to-real and synthetic-to-real. Real-to- real adaptation aims to generalize models across real sce- narios [8, 15], but since one real-world dataset is taken as the source ...
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...
PyTorch implementations of the paper: "Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting. (T-NNLS, 2021)..." domain-adaptationcrowd-counting Readme Activity 16stars 3watching 4forks Releases No releases published Packages ...
Code for the paper "Leveraging Self-Supervision for Cross-Domain Crowd Counting"(CVPR 2022) - weizheliu/Cross-Domain-Crowd-Counting
The problem of domain-adaptation in crowd counting is also addressed by training a model using the abundant labelled data available in the source domain and transferring the parameters learnt to a target domain with relatively fewer labelled data using neuron linear transformation, thereby minimizing ...
Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation Cross-Domain Multi-Task Learning for Object Detection and Saliency Estimation Error-aware density isomorphism reconstruction for unsupervised cross-domain crowd counting Domain structure-based transfer learning for cross-domain word representat...