1 code implementation • 25 Nov 2022 • Jing Xu, Wentao Shi, Pan Gao, Zhengwei Wang, Qizhu Li
On the more challenging ADE20K dataset, our best model yields a single-scale mIoU of 50. 18, and a multi-scale mIoU of 51. 8, which is on-par with the current state-of-art model, while we drastically cut the number of FLOPs by 53. 5%.
1 code implementation • 17 Oct 2021 • Zhengwei Wang, Qi She, Aljosa Smolic
Video compression (e. g., H. 264, MPEG-4) reduces superfluous information by representing the raw video stream using the concept of Group of Pictures (GOP).
1 code implementation • 23 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.
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.
no code implementations • 26 Apr 2020 • Qi She, Fan Feng, Qi Liu, Rosa H. M. Chan, Xinyue Hao, Chuanlin Lan, Qihan Yang, Vincenzo Lomonaco, German I. Parisi, Heechul Bae, Eoin Brophy, Baoquan Chen, Gabriele Graffieti, Vidit Goel, Hyonyoung Han, Sathursan Kanagarajah, Somesh Kumar, Siew-Kei Lam, Tin Lun Lam, Liang Ma, Davide Maltoni, Lorenzo Pellegrini, Duvindu Piyasena, ShiLiang Pu, Debdoot Sheet, Soonyong Song, Youngsung Son, Zhengwei Wang, Tomas E. Ward, Jianwen Wu, Meiqing Wu, Di Xie, Yangsheng Xu, Lin Yang, Qiaoyong Zhong, Liguang Zhou
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
1 code implementation • 20 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.
1 code implementation • 5 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.
2 code implementations • 15 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.
3 code implementations • 4 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.
no code implementations • 28 May 2019 • Zhengwei Wang, Qi She, Eoin Brophy, Alan F. Smeaton, Tomas E. Ward, Graham Healy
Deep neural networks (DNNs) are inspired from the human brain and the interconnection between the two has been widely studied in the literature.
1 code implementation • 10 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.
no code implementations • 14 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.
no code implementations • 15 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).
no code implementations • 3 Dec 2018 • Eoin Brophy, José Juan Dominguez Veiga, Zhengwei Wang, Alan F. Smeaton, Tomas E. Ward
We then use the 2048 dimensional features from the penultimate layer as input to a support vector machine.
no code implementations • 10 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.