67 citations · 85 across the 9 of their papers we have counts for
9 papers
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks
Jie Wei, Zhigang Zhu, Erik Blasch +5
During natural disasters, aircraft and satellites are used to survey the impacted regions. Usually human experts are needed to manually label the degrees of the building damage so…
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural Network
Ling Zhang, Zhigang Zhu
To alleviate the cost of collecting and annotating large-scale point cloud datasets, we propose an unsupervised learning approach to learn features from unlabeled point cloud "3D o…
Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling
Greg Olmschenk, Hao Tang, Zhigang Zhu
Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, manageme…
Generalizing semi-supervised generative adversarial networks to regression using feature contrasting
Greg Olmschenk, Zhigang Zhu, Hao Tang
In this work, we generalize semi-supervised generative adversarial networks (GANs) from classification problems to regression problems. In the last few years, the importance of imp…
Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing
Wei Li, Farnaz Abitahi, Zhigang Zhu
Action Unit (AU) detection becomes essential for facial analysis. Many proposed approaches face challenging problems in dealing with the alignments of different face regions, in th…
EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection
Wei Li, Farnaz Abtahi, Zhigang Zhu +1
In this paper, we propose a deep learning based approach for facial action unit detection by enhancing and cropping the regions of interest. The approach is implemented by adding t…
Learning and Fusing Multimodal Features from and for Multi-task Facial Computing
Wei Li, Zhigang Zhu
We propose a deep learning-based feature fusion approach for facial computing including face recognition as well as gender, race and age detection. Instead of training a single cla…
A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset
Wei Li, Christina Tsangouri, Farnaz Abtahi +1
In this paper, we propose a recursive framework to recognize facial expressions from images in real scenes. Unlike traditional approaches that typically focus on developing and ref…
Towards an "In-the-Wild" Emotion Dataset Using a Game-based Framework
Wei Li, Farnaz Abtahi, Christina Tsangouri +1
In order to create an "in-the-wild" dataset of facial emotions with large number of balanced samples, this paper proposes a game-based data collection framework. The framework main…