Search Results for author: Rui Zeng

Found 17 papers, 9 papers with code

Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction

no code implementations2 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.

Automatic Modulation Recognition Data Augmentation

Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems

1 code implementation15 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.

Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems

1 code implementation5 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.

Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback

1 code implementation5 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.

Quantization

Better Lightweight Network for Free: Codeword Mimic Learning for Massive MIMO CSI feedback

1 code implementation29 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.

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

1 code implementation16 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.

Brain Computer Interface EEG +1

Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition

no code implementations28 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.

Emotion Recognition

MTRNet++: One-stage Mask-based Scene Text Eraser

1 code implementation16 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.

Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation

1 code implementation11 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.

Data Augmentation Image-to-Image Translation +1

Rethinking Planar Homography Estimation Using Perspective Fields

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.

Homography Estimation

Geometry-constrained Car Recognition Using a 3D Perspective Network

no code implementations19 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.

MTRNet: A Generic Scene Text Eraser

1 code implementation11 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.

Curved Text Detection Text Detection

Kernel principal component analysis network for image classification

no code implementations20 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.

Classification Face Recognition +3

Color Image Classification via Quaternion Principal Component Analysis Network

no code implementations5 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.

Classification General Classification +1

Multilinear Principal Component Analysis Network for Tensor Object Classification

no code implementations5 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.

Classification General Classification +1

Tensor object classification via multilinear discriminant analysis network

no code implementations5 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.

Classification General Classification +1

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