Search Results for author: Zhiqiang Wei

Found 17 papers, 2 papers with code

LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition

no code implementations6 Mar 2024 Jialu Shi, Zhiqiang Wei, Jie Nie, Lei Huang

In this paper, we present to incorporate the subtle local fine-grained feature learning into global self-supervised contrastive learning through a pure self-supervised global-local fine-grained contrastive learning framework.

Contrastive Learning Fine-Grained Visual Recognition +3

LR-CNN: Lightweight Row-centric Convolutional Neural Network Training for Memory Reduction

no code implementations21 Jan 2024 Zhigang Wang, Hangyu Yang, Ning Wang, Chuanfei Xu, Jie Nie, Zhiqiang Wei, Yu Gu, Ge Yu

However, training its complex network is very space-consuming, since a lot of intermediate data are preserved across layers, especially when processing high-dimension inputs with a big batch size.

Optimal BER Minimum Precoder Design for OTFS-Based ISAC Systems

no code implementations19 Dec 2023 Jun Wu, Weijie Yuan, Zhiqiang Wei, Jinjin Yan, Derrick Wing Kwan Ng

This paper investigates the bit error rate (BER) minimum pre-coder design for an orthogonal time frequency space (OTFS)-based integrated sensing and communications (ISAC) system, which is considered as a promising technique for enabling future wireless networks.

Direction-of-Arrival Estimation for Constant Modulus Signals Using a Structured Matrix Recovery Technique

no code implementations15 Jul 2023 Xunmeng Wu, Zai Yang, Zhiqiang Wei, Zongben Xu

This paper addresses the problem of direction-of-arrival (DOA) estimation for constant modulus (CM) source signals using a uniform or sparse linear array.

Direction of Arrival Estimation

Scale-Semantic Joint Decoupling Network for Image-text Retrieval in Remote Sensing

no code implementations12 Dec 2022 Chengyu Zheng, Ning Song, Ruoyu Zhang, Lei Huang, Zhiqiang Wei, Jie Nie

To address these issues, we propose a novel Scale-Semantic Joint Decoupling Network (SSJDN) for remote sensing image-text retrieval.

Cross-Modal Retrieval Retrieval +1

Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach

no code implementations23 Nov 2022 Chang Liu, Xuemeng Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Robert Schober

With the proposed predictive approach, we can avoid full-scale CSI estimation and facilitate low-dimensional CE for transmit beamforming design such that the signaling overhead is reduced by a scale of $\frac{1}{N}$, where $N$ is the number of IRS elements.

Integrated Sensing and Communication-assisted Orthogonal Time Frequency Space Transmission for Vehicular Networks

no code implementations7 May 2021 Weijie Yuan, Zhiqiang Wei, Shuangyang Li, Jinhong Yuan, Derrick Wing Kwan Ng

Benefiting from the OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously transmitting downlink information to the vehicles and estimating the sensing parameters of vehicles, e. g., locations and speeds, based on the reflected echoes.

Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems

no code implementations14 Jan 2021 Zhiqiang Wei, Weijie Yuan, Shuangyang Li, Jinhong Yuan, Derrick Wing Kwan Ng

OTFS channel estimation is first formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation.

Information Theory Information Theory

Cross Domain Iterative Detection for Orthogonal Time Frequency Space Modulation

no code implementations11 Jan 2021 Shuangyang Li, Weijie Yuan, Zhiqiang Wei, Jinhong Yuan

Different from conventional OTFS detection methods, the proposed algorithm applies basic estimation/detection approaches to both the time domain and delay-Doppler (DD) domain and iteratively updates the extrinsic information from two domains with the unitary transformation.

Information Theory Information Theory

Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications

no code implementations10 Nov 2020 Chang Liu, Xuemeng Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Jinhong Yuan, Ying-Chang Liang

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI).

TAG Transfer Learning

Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach

no code implementations16 Sep 2020 Chang Liu, Weijie Yuan, Zhiqiang Wei, Xuemeng Liu, Derrick Wing Kwan Ng

Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications.

PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field

no code implementations13 Sep 2020 Haixia Bi, Jing Yao, Zhiqiang Wei, Danfeng Hong, Jocelyn Chanussot

Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications.

Classification Data Augmentation +2

Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

no code implementations11 Sep 2020 Chang Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Jinhong Yuan, Ying-Chang Liang

To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols.

TAG Transfer Learning

Sum-Rate Maximization for Multiuser MISO Downlink Systems with Self-sustainable IRS

no code implementations24 May 2020 Shaokang Hu, Zhiqiang Wei, Yuanxin Cai, Derrick Wing Kwan Ng, Jinhong Yuan

This paper investigates multiuser multi-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can harvest power from the received signals.

UW-NET: AN INCEPTION-ATTENTION NETWORK FOR UNDERWATER IMAGE CLASSIFICATION

no code implementations ICLR 2020 Miao Yang and Ke Hu, Chongyi Li, Zhiqiang Wei

By substituting the inception module with the I-A module, the Inception-ResnetV2 network achieves a 10. 7% top1 error rate and a 0% top5 error rate on the subset of ILSVRC-2012, which further illustrates the function of the background attention in the image classifications.

Classification General Classification +2

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