no code implementations • 2 Jan 2024 • Yunpeng Qu, Zhilin Lu, Rui Zeng, Jintao Wang, Jian Wang
Modulated signals exhibit long temporal dependencies, and extracting global features is crucial in identifying modulation schemes.
1 code implementation • 15 Feb 2023 • Zhilin Lu, Xudong Zhang, Rui Zeng, Jintao Wang
Hybrid beamforming is widely recognized as an important technique for millimeter wave (mmWave) multiple input multiple output (MIMO) systems.
1 code implementation • 5 Feb 2023 • Zhilin Lu, Xudong Zhang, Rui Zeng, Jintao Wang
Channel state information (CSI) feedback is necessary for the frequency division duplexing (FDD) multiple input multiple output (MIMO) systems due to the channel non-reciprocity.
1 code implementation • 5 Nov 2022 • Xudong Zhang, Zhilin Lu, Rui Zeng, Jintao Wang
In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback.
1 code implementation • 29 Oct 2022 • Zhilin Lu, Xudong Zhang, Rui Zeng, Jintao Wang
In this paper, a cost free distillation technique named codeword mimic (CM) is proposed to train better feedback networks with the practical lightweight encoder.
1 code implementation • 16 Jun 2020 • Yimin Hou, Shuyue Jia, Xiangmin Lun, Ziqian Hao, Yan Shi, Yang Li, Rui Zeng, Jinglei Lv
To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.
no code implementations • 28 Apr 2020 • Dung Nguyen, Duc Thanh Nguyen, Rui Zeng, Thanh Thi Nguyen, Son N. Tran, Thin Nguyen, Sridha Sridharan, Clinton Fookes
Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area.
no code implementations • 24 Mar 2020 • Dung Nguyen, Sridha Sridharan, Duc Thanh Nguyen, Simon Denman, Son N. Tran, Rui Zeng, Clinton Fookes
Deep learning has been applied to achieve significant progress in emotion recognition.
1 code implementation • 16 Dec 2019 • Osman Tursun, Simon Denman, Rui Zeng, Sabesan Sivapalan, Sridha Sridharan, Clinton Fookes
The results of ablation studies demonstrate that the proposed multi-branch architecture with attention blocks is effective and essential.
1 code implementation • 11 Oct 2019 • Yunyan Xing, ZongYuan Ge, Rui Zeng, Dwarikanath Mahapatra, Jarrel Seah, Meng Law, Tom Drummond
We demonstrate the effectiveness of our model on two tasks: (i) we invite certified radiologists to assess the quality of the generated synthetic images against real and other state-of-the-art generative models, and (ii) data augmentation to improve the performance of disease localisation.
1 code implementation • ACCV 2018 2019 • Rui Zeng, Simon Denman, Sridha Sridharan, Clinton Fookes
In addition, the new parameterization of this task is general and can be implemented by any fully convolutional network (FCN) architecture.
Ranked #1 on Homography Estimation on COCO 2014
no code implementations • 19 Mar 2019 • Rui Zeng, ZongYuan Ge, Simon Denman, Sridha Sridharan, Clinton Fookes
Unlike existing methods which only use attention mechanisms to locate 2D discriminative information, our work learns a novel 3D perspective feature representation of a vehicle, which is then fused with 2D appearance feature to predict the category.
1 code implementation • 11 Mar 2019 • Osman Tursun, Rui Zeng, Simon Denman, Sabesan Sivapalan, Sridha Sridharan, Clinton Fookes
Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multi-lingual and curved text detection and inpainting.
no code implementations • 20 Dec 2015 • Dan Wu, Jiasong Wu, Rui Zeng, Longyu Jiang, Lotfi Senhadji, Huazhong Shu
In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed.
no code implementations • 5 Mar 2015 • Rui Zeng, Jiasong Wu, Zhuhong Shao, Yang Chen, Lotfi Senhadji, Huazhong Shu
The Principal Component Analysis Network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases.
no code implementations • 5 Nov 2014 • Rui Zeng, Jiasong Wu, Lotfi Senhadji, Huazhong Shu
The MLDANet is a variation of linear discriminant analysis network (LDANet) and principal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms.
no code implementations • 5 Nov 2014 • Rui Zeng, Jiasong Wu, Zhuhong Shao, Lotfi Senhadji, Huazhong Shu
The recently proposed principal component analysis network (PCANet) has been proved high performance for visual content classification.