|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.
Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations.
SOTA for Crowd Counting on UCF CC 50
The task of crowd counting is to automatically estimate the pedestrian number in crowd images.
In this work, we explore the cross-scale similarity in crowd counting scenario, in which the regions of different scales often exhibit high visual similarity.
Crowd counting on static images is a challenging problem due to scale variations.
Deep convolutional networks (ConvNets) have achieved unprecedented performances on many computer vision tasks.
In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image.
Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion.
Adding HR to a simple VGG front-end improves performance on all these benchmarks compared to a simple one-look baseline model and results in state-of-the-art performance for car counting.