Search Results for author: Zhengwei Wang

Found 12 papers, 7 papers with code

Generative adversarial networks in time series: A survey and taxonomy

1 code implementation23 Jul 2021 Eoin Brophy, Zhengwei Wang, Qi She, Tomas Ward

We propose a taxonomy of discrete-variant GANs and continuous-variant GANs, in which GANs deal with discrete time series and continuous time series data.

Time Series

ACTION-Net: Multipath Excitation for Action Recognition

1 code implementation CVPR 2021 Zhengwei Wang, Qi She, Aljosa Smolic

To this end, we propose a spAtio-temporal, Channel and moTion excitatION (ACTION) module consisting of three paths: Spatio-Temporal Excitation (STE) path, Channel Excitation (CE) path, and Motion Excitation (ME) path.

Action Recognition

CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture Recognition

1 code implementation20 Apr 2020 Zhengwei Wang, Qi She, Tejo Chalasani, Aljosa Smolic

Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses.

Gesture Recognition Video Recognition

A Neuro-AI Interface for Evaluating Generative Adversarial Networks

1 code implementation5 Mar 2020 Zhengwei Wang, Qi She, Alan F. Smeaton, Tomas E. Ward, Graham Healy

In this work, we introduce an evaluation metric called Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals.

Speech Synthesis

OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning

2 code implementations15 Nov 2019 Qi She, Fan Feng, Xinyue Hao, Qihan Yang, Chuanlin Lan, Vincenzo Lomonaco, Xuesong Shi, Zhengwei Wang, Yao Guo, Yimin Zhang, Fei Qiao, Rosa H. M. Chan

Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks.

Object Recognition

Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy

3 code implementations4 Jun 2019 Zhengwei Wang, Qi She, Tomas E. Ward

While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision.

Image Inpainting Image Quality Assessment +3

Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks

1 code implementation10 May 2019 Zhengwei Wang, Qi She, Alan F. Smeaton, Tomas E. Ward, Graham Healy

In this work, we describe an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals.

Image Generation Speech Synthesis

Quick and Easy Time Series Generation with Established Image-based GANs

no code implementations14 Feb 2019 Eoin Brophy, Zhengwei Wang, Tomas E. Ward

In the recent years Generative Adversarial Networks (GANs) have demonstrated significant progress in generating authentic looking data.

Time Series

Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

no code implementations15 Jan 2019 Zhengwei Wang, Graham Healy, Alan F. Smeaton, Tomas E. Ward

In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR).

EEG General Classification +1

Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation

no code implementations10 Nov 2018 Zhengwei Wang, Graham Healy, Alan F. Smeaton, Tomas E. Ward

We propose a novel approach that combines a brain-computer interface (BCI) with GANs to generate a measure we call Neuroscore, which closely mirrors the behavioral ground truth measured from participants tasked with discerning real from synthetic images.

Image Generation

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