95 citations · 362 across the 17 of their papers we have counts for
17 papers
Occlusion-Robust FAU Recognition by Mining Latent Space of Masked Autoencoders
Minyang Jiang, Yongwei Wang, Martin J. McKeown +1
Facial action units (FAUs) are critical for fine-grained facial expression analysis. Although FAU detection has been actively studied using ideally high quality images, it was not…
Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey
Xun Chen, Chang Li, Aiping Liu +3
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity. Tradition…
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation
Mohsen Gholami, Bastian Wandt, Helge Rhodin +2
This paper addresses the problem of cross-dataset generalization of 3D human pose estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a major per…
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification
Jianzhe Lin, Tianze Yu, Z. Jane Wang
Annotated images are required for both supervised model training and evaluation in image classification. Manually annotating images is arduous and expensive, especially for multi-l…
Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework
Li Ding, Yongwei Wang, Xin Ding +4
Deep learning based image classification models are shown vulnerable to adversarial attacks by injecting deliberately crafted noises to clean images. To defend against adversarial…
TriPose: A Weakly-Supervised 3D Human Pose Estimation via Triangulation from Video
Mohsen Gholami, Ahmad Rezaei, Helge Rhodin +2
Estimating 3D human poses from video is a challenging problem. The lack of 3D human pose annotations is a major obstacle for supervised training and for generalization to unseen da…
Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression
Xin Ding, Yongwei Wang, Zuheng Xu +2
Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a…
Multi-view 3D Reconstruction with Transformer
Dan Wang, Xinrui Cui, Xun Chen +5
Deep CNN-based methods have so far achieved the state of the art results in multi-view 3D object reconstruction. Despite the considerable progress, the two core modules of these me…
Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection
Mazen Abdelfattah, Kaiwen Yuan, Z. Jane Wang +1
Most autonomous vehicles (AVs) rely on LiDAR and RGB camera sensors for perception. Using these point cloud and image data, perception models based on deep neural nets (DNNs) have…
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models
Mazen Abdelfattah, Kaiwen Yuan, Z. Jane Wang +1
We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. DNNs have achieved h…
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification
Yongwei Wang, Mingquan Feng, Rabab Ward +2
Deep neural networks are vulnerable to adversarial attacks. White-box adversarial attacks can fool neural networks with small adversarial perturbations, especially for large size i…
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection
Yongwei Wang, Xin Ding, Li Ding +2
Recently, generative adversarial networks (GANs) can generate photo-realistic fake facial images which are perceptually indistinguishable from real face photos, promoting research…
A Deep Learning Based Attack for The Chaos-based Image Encryption
Chen He, Kan Ming, Yongwei Wang +1
In this letter, as a proof of concept, we propose a deep learning-based approach to attack the chaos-based image encryption algorithm in \cite{guan2005chaos}. The proposed method f…
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction
Ramy Hussein, Mohamed Osama Ahmed, Rabab Ward +3
Objective: The aim of this study is to develop an efficient and reliable epileptic seizure prediction system using intracranial EEG (iEEG) data, especially for people with drug-res…
Joint Correction of Attenuation and Scatter Using Deep Convolutional Neural Networks (DCNN) for Time-of-Flight PET
Jaewon Yang, Dookun Park, Jae Ho Sohn +3
Deep convolutional neural networks (DCNN) have demonstrated its capability to convert MR image to pseudo CT for PET attenuation correction in PET/MRI. Conventionally, attenuated ev…
Epileptic Seizure Detection: A Deep Learning Approach
Ramy Hussein, Hamid Palangi, Rabab Ward +1
Epilepsy is the second most common brain disorder after migraine. Automatic detection of epileptic seizures can considerably improve the patients' quality of life. Current Electroe…
Multimodal deep learning approach for joint EEG-EMG data compression and classification
Ahmed Ben Said, Amr Mohamed, Tarek Elfouly +2
In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach. Specifically, we build our system based on the deep…