An end-to-end transformer model for crowd localization
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 …
simply counting. Existing methods employ pseudo-bounding boxes or pre-designed …
Transcrowd: weakly-supervised crowd counting with transformers
The mainstream crowd counting methods usually utilize the convolution neural network
(CNN) to regress a density map, requiring point-level annotations. However, annotating …
(CNN) to regress a density map, requiring point-level annotations. However, annotating …
Steerer: Resolving scale variations for counting and localization via selective inheritance learning
Scale variation is a deep-rooted problem in object counting, which has not been effectively
addressed by existing scale-aware algorithms. An important factor is that they typically …
addressed by existing scale-aware algorithms. An important factor is that they typically …
Optimal transport minimization: Crowd localization on density maps for semi-supervised counting
The accuracy of crowd counting in images has improved greatly in recent years due to the
development of deep neural networks for predicting crowd density maps. However, most …
development of deep neural networks for predicting crowd density maps. However, most …
Crowdclip: Unsupervised crowd counting via vision-language model
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 …
expensive, especially in dense scenes. To alleviate the problem, we propose a novel …
CGINet: Cross-modality grade interaction network for RGB-T crowd counting
Crowd counting is a fundamental and challenging task that requires rich information to
generate a pixel-level crowd density map. Additionally, the development of thermal sensing …
generate a pixel-level crowd density map. Additionally, the development of thermal sensing …
Deep learning in crowd counting: A survey
Counting high‐density objects quickly and accurately is a popular area of research. Crowd
counting has significant social and economic value and is a major focus in artificial …
counting has significant social and economic value and is a major focus in artificial …
CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion Models
Y Ranasinghe, NG Nair… - Proceedings of the …, 2024 - openaccess.thecvf.com
Crowd counting is a fundamental problem in crowd analysis which is typically accomplished
by estimating a crowd density map and summing over the density values. However this …
by estimating a crowd density map and summing over the density values. However this …
Congested crowd instance localization with dilated convolutional swin transformer
Crowd localization is a new computer vision task, evolved from crowd counting. Different
from the latter, it provides more precise location information for each instance, not just …
from the latter, it provides more precise location information for each instance, not just …
MAFusion: Multiscale attention network for infrared and visible image fusion
X Li, H Chen, Y Li, Y Peng - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The infrared and visible image fusion aims to generate one image with rich information by
integrating thermal regions from the infrared image and texture details from the visible …
integrating thermal regions from the infrared image and texture details from the visible …