Author name cluster

Wei Ren

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7 papers

2 author rows

EAAI Journal 2026 Journal Article

A Center-Focused Transformer for hyperspectral image classification

Chaoxu Yang
Jia Duan
Xi Liu
Lianchong Zhang
Jiangbing Sun
Yan Zhang
Wei Ren

In recent years, transformer-based methods have achieved remarkable progress in hyperspectral image classification (HSIC). However, they often rely heavily on extensive training samples to achieve optimal performance. Moreover, these methods frequently fail to adequately capture diverse local spectral–spatial correlations and multi-granular features inherent in hyperspectral images (HSIs). Crucially, existing approaches often overlook the pivotal role of the target center pixel. Their attention mechanisms tend to focus on irrelevant background regions, thereby reducing feature discriminability and degrading classification accuracy. To address these challenges, we propose a novel Center-Focused Transformer (CFT) framework that seamlessly integrates multi-scale spectral–spatial fusion for HSIC. Our framework comprises three key components. First, the Spectral–Spatial Fusion (SSF) mechanism integrates local and global dependencies by employing PCA alongside a Superpixel Graph Feature Extraction (SGFE) block. Second, the Multi-Granular Feature Enhancement (MGFE) approach strengthens spectral–spatial interactions through patch augmentation, a HybridConv block, and a Multi-Scale CBAM (MS-CBAM) block. Finally, the Focus Center Transformer (FCT) strategy explicitly emphasizes the importance of the central pixel for precise classification by incorporating Gaussian Positional Embedding (GPE) and cross-layer aggregation. Extensive experiments on four public datasets demonstrate that the proposed CFT consistently outperforms state-of-the-art methods, highlighting its potential for practical engineering applications. • A novel Center-Focused Transformer (CFT) framework is proposed for hyperspectral image classification. • The CFT model integrates a Spectral–Spatial Fusion (SSF) mechanism to effectively capture local and global dependencies. • A Multi-Granular Feature Enhancement (MGFE) approach is introduced to model multi-scale features in both spectral and spatial dimensions. • A Focus Center Transformer (FCT) strategy with Gaussian positional embedding is proposed to improve classification accuracy. • The CFT consistently outperforms state-of-the-art methods on four public datasets, showcasing its potential for engineering applications.

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JBHI Journal 2026 Journal Article

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise

Mengwen Ye
Yingzi Huangfu
Shujian Gao
Wei Ren
Weifan Liu
Zekuan Yu

Federated Learning (FL) emerged as a solution for collaborative medical image classification while preserving data privacy. However, label noise, which arises from inter-institutional data variability, can cause training instability and degrade model performance. Existing FL methods struggle with noise heterogeneity and the imbalance in medical data. Motivated by these challenges, we propose FedGSCA, a novel framework for enhancing robustness in noisy medical FL. FedGSCA introduces a Global Sample Selector that aggregates noise knowledge from all clients, effectively addressing noise heterogeneity and improving global model stability. Furthermore, we develop a Client Adaptive Adjustment (CAA) mechanism that combines adaptive threshold pseudo-label generation and Robust Credal Labeling Loss. CAA dynamically adjusts to class distributions, ensuring the inclusion of minority samples and carefully managing noisy labels by considering multiple plausible labels. This dual approach mitigates the impact of noisy data and prevents overfitting during local training, which improves the generalizability of the model. We evaluate FedGSCA on one real-world colon slides dataset and two synthetic medical datasets under various noise conditions, including symmetric, asymmetric, extreme, and heterogeneous types. The results show that FedGSCA outperforms the state-of-the-art methods, excelling in extreme and heterogeneous noise scenarios. Moreover, FedGSCA demonstrates significant advantages in improving model stability and handling complex noise, making it well-suited for real-world medical federated learning scenarios.

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IROS Conference 2025 Conference Paper

PLK-Calib: Single-shot and Target-less LiDAR-Camera Extrinsic Calibration using Plücker Lines

Yanyu Zhang
Jie Xu
Wei Ren

Accurate LiDAR-Camera (LC) calibration is challenging but crucial for autonomous systems and robotics. In this paper, we propose two single-shot and target-less algorithms to estimate the calibration parameters between LiDAR and camera using line features. The first algorithm constructs line-to-line constraints by defining points-to-line projection errors and minimizes the projection error. The second algorithm (PLK-Calib) utilizes the co-perpendicular and co-parallel geometric properties of lines in Plücker (PLK) coordinate, and decouples the rotation and translation into two constraints, enabling more accurate estimates. Our degenerate analysis and Monte Carlo simulation indicate that three nonparallel line pairs are the minimal requirements to estimate the extrinsic parameters. Furthermore, we collect an LC calibration dataset with varying extrinsic under three different scenarios and use it to evaluate the performance of our proposed algorithms.

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EAAI Journal 2023 Journal Article

A lightweight privacy-preserving scheme using pixel block mixing for facial image classification in deep learning

Yuexin Xiang
Tiantian Li
Wei Ren
Tianqing Zhu
Kim-Kwang Raymond Choo

The training of state-of-the-art deep learning models generally requires significant high-quality data, including personal and sensitive data. To ensure privacy of the sensitive data used in training deep learning models, many methods have been designed by the research community. However, it has been observed that many privacy-preserving approaches for image-based deep learning model training incur significant time in the processing of images and/or have low accuracy on trained models when a large number of images is used for classification tasks. Hence, in this paper, we propose a lightweight and efficient approach to preserve image privacy while maintaining the availability of the training set. Specifically, we design the pixel block mixing algorithm for facial image classification privacy preservation in deep learning. Experimental findings show that the models trained by the mixed training set generated by the proposed algorithm maintain their availability. The comparison results of the structural similarity index measure between images in the new training set and the original training set show that our scheme preserves image privacy. Our evaluations also reveal that the data augmentation can be applied to the mixed training set to improve the training effectiveness. We also demonstrate it is computationally challenging for attackers to restore the mixed training set to the original one.

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AAAI Conference 2023 Conference Paper

Fast Fluid Simulation via Dynamic Multi-Scale Gridding

Jinxian Liu
Ye Chen
Bingbing Ni
Wei Ren
Zhenbo Yu
Xiaoyang Huang

Recent works on learning-based frameworks for Lagrangian (i.e., particle-based) fluid simulation, though bypassing iterative pressure projection via efficient convolution operators, are still time-consuming due to excessive amount of particles. To address this challenge, we propose a dynamic multi-scale gridding method to reduce the magnitude of elements that have to be processed, by observing repeated particle motion patterns within certain consistent regions. Specifically, we hierarchically generate multi-scale micelles in Euclidean space by grouping particles that share similar motion patterns/characteristics based on super-light motion and scale estimation modules. With little internal motion variation, each micelle is modeled as a single rigid body with convolution only applied to a single representative particle. In addition, a distance-based interpolation is conducted to propagate relative motion message among micelles. With our efficient design, the network produces high visual fidelity fluid simulations with the inference time to be only 4.24 ms/frame (with 6K fluid particles), hence enables real-time human-computer interaction and animation. Experimental results on multiple datasets show that our work achieves great simulation acceleration with negligible prediction error increase.

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ICRA Conference 2014 Conference Paper

On-board inertial-assisted visual odometer on an embedded system

Guyue Zhou
Jiaxin Ye
Wei Ren
Tao Wang
Zexiang Li 0001

In this paper, we propose a novel inertial-assisted visual odometry system intended for low-cost micro aerial vehicles (MAVs). The system sensor assembly consists of two downward-facing cameras and an inertial measurement unit (IMU) with three-axis accelerometers/gyroscopes. Real-time implementation of the system is enabled by a low-cost embedded system via two important features: firstly, simple pixel-level algorithms are integrated in a low-end FPGA and accelerated via pipeline and combinational logic techniques; secondly, a fast yaw-and-translation estimation algorithm works well with a novel outlier rejection scheme based on probabilistic predetermined operations rather than hypothesis testing iterations. We illustrate the performance of our system by hovering a MAV in a GPS-denied environment. Its feasibility and robustness is also illustrated in complex outdoor environments.

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TCS Journal 2011 Journal Article

A note on ‘Algorithms for connected set cover problem and fault-tolerant connected set cover problem’

Wei Ren
Qing Zhao

A flaw in the greedy approximation algorithm proposed by Zhang et al. (2009) [1] for the minimum connected set cover problem is corrected, and a stronger result on the approximation ratio of the modified greedy algorithm is established. The results are now consistent with the existing results on the connected dominating set problem which is a special case of the minimum connected set cover problem.

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