activity
20162022
most citedCrowd counting with crowd attention convolutional neural network

80 citations · 129 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV202218 cited

SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing

Jiwei Chen, Kewei Wang, Wen Su +1

Crowd counting based on density maps is generally regarded as a regression task.Deep learning is used to learn the mapping between image content and crowd density distribution. Alt…

cs.CV202213 cited

Crowd counting with segmentation attention convolutional neural network

Jiwei Chen, Zengfu Wang

Deep learning occupies an undisputed dominance in crowd counting. In this paper, we propose a novel convolutional neural network (CNN) architecture called SegCrowdNet. Despite the…

cs.CV202280 cited

Crowd counting with crowd attention convolutional neural network

Jiwei Chen, Wen Su, Zengfu Wang

Crowd counting is a challenging problem due to the scene complexity and scale variation. Although deep learning has achieved great improvement in crowd counting, scene complexity a…

cs.CV20211 cited

On Exploring and Improving Robustness of Scene Text Detection Models

Shilian Wu, Wei Zhai, Yongrui Li +2

It is crucial to understand the robustness of text detection models with regard to extensive corruptions, since scene text detection techniques have many practical applications. Fo…

cs.CV20212 cited

Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation

Jia Li, Linhua Xiang, Jiwei Chen +1

We propose a simple yet reliable bottom-up approach with a good trade-off between accuracy and efficiency for the problem of multi-person pose estimation. Given an image, we employ…

cs.CV201910 cited

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

Jia Li, Wen Su, Zengfu Wang

We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one. The improved approach surpasses the baseline significantly thanks to (1) an…