1 code implementation • 6 Feb 2024 • Mingyue Guo, Binghui Chen, Zhaoyi Yan, YaoWei Wang, Qixiang Ye
Multidomain crowd counting aims to learn a general model for multiple diverse datasets.
no code implementations • 4 Dec 2023 • Mingyue Guo, Li Yuan, Zhaoyi Yan, Binghui Chen, YaoWei Wang, Qixiang Ye
In this study, we propose mutual prompt learning (mPrompt), which leverages a regressor and a segmenter as guidance for each other, solving bias and inaccuracy caused by annotation variance while distinguishing foreground from background.
1 code implementation • 16 Jun 2022 • Xin Zhong, Zhaoyi Yan, Jing Qin, WangMeng Zuo, Weigang Lu
However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information.
1 code implementation • ICCV 2021 • Binghui Chen, Zhaoyi Yan, Ke Li, Pengyu Li, Biao Wang, WangMeng Zuo, Lei Zhang
In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc.
1 code implementation • 8 Jul 2021 • Zhaoyi Yan, Ruimao Zhang, Hongzhi Zhang, Qingfu Zhang, WangMeng Zuo
One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect.
1 code implementation • ICCV 2019 • Zhaoyi Yan, Yuchen Yuan, WangMeng Zuo, Xiao Tan, Yezhen Wang, Shilei Wen, Errui Ding
In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i. e. PGCNet), which aims to overcome the dramatic intra-scene scale variations of people due to the perspective effect.
2 code implementations • ECCV 2018 • Zhaoyi Yan, Xiaoming Li, Mu Li, WangMeng Zuo, Shiguang Shan
To this end, the encoder feature of the known region is shifted to serve as an estimation of the missing parts.