270 citations · 276 across the 4 of their papers we have counts for
4 papers
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model
Dingkang Liang, Jiahao Xie, Zhikang Zou +3
Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsup…
Super-Resolution Information Enhancement For Crowd Counting
Jiahao Xie, Wei Xu, Dingkang Liang +5
Crowd counting is a challenging task due to the heavy occlusions, scales, and density variations. Existing methods handle these challenges effectively while ignoring low-resolution…
An End-to-End Transformer Model for Crowd Localization
Dingkang Liang, Wei Xu, Xiang Bai
Crowd localization, predicting head positions, is a more practical and high-level task than simply counting. Existing methods employ pseudo-bounding boxes or pre-designed localizat…
TransCrowd: weakly-supervised crowd counting with transformers
Dingkang Liang, Xiwu Chen, Wei Xu +2
The mainstream crowd counting methods usually utilize the convolution neural network (CNN) to regress a density map, requiring point-level annotations. However, annotating each per…