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Wei Xi

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11 papers

2 author rows

TIST Journal 2026 Journal Article

FedPRS: A Privacy-preserving Representation Synthesis Framework for Federated Contribution Evaluation

Yuwei Fan
Yuan Yao
Wei Xi
Quan Zhao
Zelei Liu
Lixin Fan
Qiang Yang
Jian Jin

Federated Learning (FL) enables the collaborative training of a global model while protecting participants’ privacy. Evaluating each participant’s contribution is essential to providing a high-quality model, ensuring fairness, and mitigating potential biases. Most existing contribution evaluation approaches for FL assume that the server has a public validation dataset. However, it is almost impossible to obtain a validation dataset due to privacy concerns. In this article, we propose a Federated Privacy-preserving Representation Synthesis (FedPRS) framework to synthesize a validation dataset for contribution evaluation. The proposed FedPRS framework first transforms each participant’s private validation dataset into its representation. Then, a random-region desensitization strategy is developed to further desensitize the dataset without compromising its utility. The desensitized representation dataset of each participant is collected by the server to evaluate federated contribution, which considers both equity and privacy protection. Moreover, we instantiate and integrate three specific contribution evaluation approaches in this framework. We perform experiments on various FL settings, including independently identically distributed (IID) and non-IID data distributions. Experimental results demonstrate that the contribution evaluation results obtained using the validation dataset synthesized by the FedPRS framework are closely aligned with those obtained using a real, private validation dataset.

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AAAI Conference 2026 Conference Paper

Spontaneous Yet Predictable: Shapelet-Driven, Channel-Aware Intention Decoding from Multi-Region ECoG

Keren Cao
Yuhang Tian
Kaizhong Zheng
Wei Xi
Xinjian Li
Liangjun Chen

Proactive intention decoding remains a critical yet underexplored challenge in brain–machine interfaces (BMIs), especially under naturalistic, self-initiated behavior. Existing systems rely on reactive decoding of motor cortex signals, resulting in substantial latency. To address this, we leverage the common marmoset’s spontaneous vocalizations and develop a high-resolution, dual-region ECoG recording paradigm targeting the prefrontal and auditory cortices and a neural decoding framework that integrates shapelet-based temporal encoding, position-aware attention, frequency-aware channel masking, contrastive clustering and a minimum error entropy-based robust loss. Our approach achieves 91.9% accuracy up to 200 ms before vocal onset—substantially outperforming 13 competitive baselines. Our model also uncovers a functional decoupling between auditory and prefrontal regions. Furthermore, joint modeling in time and frequency domains reveals novel preparatory neural signatures preceding volitional vocal output. Together, our findings bridge the gap between foundational neuroscience and applied BMI engineering, and establish a generalizable framework for intention decoding from ecologically valid, asynchronous behaviors.

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IROS Conference 2025 Conference Paper

A novel event-based structured light system for high-precision and high-speed depth sensing

Gongzhe Su
Fulong Sun
Wei Wang
Wei Xi

This paper presents a novel event-based depth sensing system with line laser scan. Our main contribution involves both hardware and software improvements to previous state-of-the-art works. The polygon mirror scanner is designed to steer line laser with a constant velocity, which minimizes non-linearity of the projected time map to improve depth precision. A piecewise linear model is then proposed to model the behavior of the scanner, which is simple and easy to calibrate. The corresponding reconstruction pipeline achieves a high-speed depth map with an efficient plane-ray intersection-based depth calculation. Experimental results verify the approach is capable of realizing 0. 6mm precision at a distance of 500mm and 8. 3ms depth reconstruction runtime on embedded platforms.

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IJCAI Conference 2025 Conference Paper

Enhancing Multimodal Model Robustness Under Missing Modalities via Memory-Driven Prompt Learning

Yihan Zhao
Wei Xi
Xiao Fu
Jizhong Zhao

Existing multimodal models typically assume the availability of all modalities, leading to significant performance degradation when certain modalities are missing. Recent methods have introduced prompt learning to adapt pretrained models to incomplete data, achieving remarkable performance when the missing cases are consistent during training and inference. However, these methods rely heavily on distribution consistency and fail to compensate for missing modalities, limiting their ability to generalize to unseen missing cases. To address this issue, we propose Memory-Driven Prompt Learning, a framework that adaptively compensates for missing modalities through prompt learning. The compensation strategies are achieved by two types of prompts: generative prompts and shared prompts. Generative prompts retrieve semantically similar samples from a predefined prompt memory that stores modality-specific semantic information, while shared prompts leverage available modalities to provide cross-modal compensation. Extensive experiments demonstrate the effectiveness of the proposed model, achieving significant improvements across diverse missing-modality scenarios, with average performance increasing from 34. 76% to 40. 40% on MM-IMDb, 62. 71% to 77. 06% on Food101, and 60. 40% to 62. 77% on Hateful Memes. The code is available at https: //github. com/zhao-yh20/MemPrompt.

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NeurIPS Conference 2025 Conference Paper

SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation

He Yang
Dongyi Lv
Song Ma
Wei Xi
Jizhong Zhao

Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent studies reveal that the condensation process can be vulnerable to backdoor attacks, where malicious triggers are injected into the condensation dataset, manipulating model behavior during inference. While prior approaches have made progress in balancing attack success rate and clean test accuracy, they often fall short in preserving stealthiness, especially in concealing the visual artifacts of condensed data or the perturbations introduced during inference. To address this challenge, we introduce \textsc{Sneakdoor}, which enhances stealthiness without compromising attack effectiveness. \textsc{Sneakdoor} exploits the inherent vulnerability of class decision boundaries and incorporates a generative module that constructs input-aware triggers aligned with local feature geometry, thereby minimizing detectability. This joint design enables the attack to remain imperceptible to both human inspection and statistical detection. Extensive experiments across multiple datasets demonstrate that \textsc{Sneakdoor} achieves a compelling balance among attack success rate, clean test accuracy, and stealthiness, substantially improving the invisibility of both the synthetic data and triggered samples while maintaining high attack efficacy. The code is available at \url{https: //github. com/XJTU-AI-Lab/SneakDoor}.

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IJCAI Conference 2024 Conference Paper

Attention Shifting to Pursue Optimal Representation for Adapting Multi-granularity Tasks

Gairui Bai
Wei Xi
Yihan Zhao
Xinhui Liu
Jizhong Zhao

Object recognition in open environments, e. g. , video surveillance, poses significant challenges due to the inclusion of unknown and multi-granularity tasks (MGT). However, recent methods exhibit limitations as they struggle to capture subtle differences between different parts within an object and adaptively handle MGT. To address this limitation, this paper proposes a Class-semantic Guided Attention Shift (SegAS) method. SegAS transforms adaptive MGT into dynamic combinations of invariant discriminant representations across different levels to effectively enhance adaptability to multi-granularity downstream tasks. Specifically, SegAS incorporates a hardness-based Attention Part Filtering Strategy (ApFS) to dynamically decompose objects into complementary parts based on the object structure and relevance to the instance. Then, SegAS shifts attention to the optimal discriminant region of each part under the guidance of hierarchical class semantics. Finally, a diversity loss is employed to emphasize the importance and distinction of different partial features. Extensive experiments validate SegAS' effectiveness in multi-granularity recognition of three tasks.

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AAAI Conference 2024 Conference Paper

FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning

Xinyuan Ji
Zhaowei Zhu
Wei Xi
Olga Gadyatskaya
Zilong Song
Yong Cai
Yang Liu

Federated Learning (FL) heavily depends on label quality for its performance. However, the label distribution among individual clients is always both noisy and heterogeneous. The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between client-specific and noisy label samples, impacting the effectiveness of existing label noise learning approaches. To tackle this issue, we propose FedFixer, where the personalized model is introduced to cooperate with the global model to effectively select clean client-specific samples. In the dual models, updating the personalized model solely at a local level can lead to overfitting on noisy data due to limited samples, consequently affecting both the local and global models’ performance. To mitigate overfitting, we address this concern from two perspectives. Firstly, we employ a confidence regularizer to alleviate the impact of unconfident predictions caused by label noise. Secondly, a distance regularizer is implemented to constrain the disparity between the personalized and global models. We validate the effectiveness of FedFixer through extensive experiments on benchmark datasets. The results demonstrate that FedFixer can perform well in filtering noisy label samples on different clients, especially in highly heterogeneous label noise scenarios.

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ICRA Conference 2024 Conference Paper

Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases

Jie Yin
Ang Li
Wei Xi
Wenxian Yu
Danping Zou

We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effective sensor anomaly detection and handling, real-time dense color mapping, and robust localization in diverse environments. We tightly integrate RGB-D images, inertial measurements, wheel odometer and GNSS signals within a factor graph to achieve accurate and reliable localization both indoors and outdoors. To ensure successful initialization, we propose an efficient strategy that comprises three different methods: stationary, visual, and dynamic, tailored to handle diverse cases. Furthermore, we develop mechanisms to detect sensor anomalies and degradation, handling them adeptly to maintain system accuracy. Our experimental results on both public and self-collected datasets demonstrate that Ground-Fusion outperforms existing low-cost SLAM systems in corner cases. We release the code and datasets at https://github.com/SJTU-ViSYS/Ground-Fusion.

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ICRA Conference 2024 Conference Paper

Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion

Ang Li
Anning Hu
Wei Xi
Wenxian Yu
Danping Zou

Accurate and dense depth estimation with stereo cameras and LiDAR is an important task for automatic driving and robotic perception. While sparse hints from LiDAR points have improved cost aggregation in stereo matching, their effectiveness is limited by the low density and non-uniform distribution. To address this issue, we propose a novel stereo-LiDAR depth estimation network with Semi-Dense hint Guidance, named SDG-Depth. Our network includes a deformable propagation module for generating a semi-dense hint map and a confidence map by propagating sparse hints using a learned deformable window. These maps then guide cost aggregation in stereo matching. To reduce the triangulation error in depth recovery from disparity, especially in distant regions, we introduce a disparity-depth conversion module. Our method is both accurate and efficient. The experimental results on benchmark tests show its superior performance. Our code is available at https://github.com/SJTU-ViSYS/SDG-Depth.

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AAAI Conference 2024 Conference Paper

UFDA: Universal Federated Domain Adaptation with Practical Assumptions

Xinhui Liu
Zhenghao Chen
Luping Zhou
Dong Xu
Wei Xi
Gairui Bai
Yihan Zhao
Jizhong Zhao

Conventional Federated Domain Adaptation (FDA) approaches usually demand an abundance of assumptions, which makes them significantly less feasible for real-world situations and introduces security hazards. This paper relaxes the assumptions from previous FDAs and studies a more practical scenario named Universal Federated Domain Adaptation (UFDA). It only requires the black-box model and the label set information of each source domain, while the label sets of different source domains could be inconsistent, and the target-domain label set is totally blind. Towards a more effective solution for our newly proposed UFDA scenario, we propose a corresponding methodology called Hot-Learning with Contrastive Label Disambiguation (HCLD). It particularly tackles UFDA's domain shifts and category gaps problems by using one-hot outputs from the black-box models of various source domains. Moreover, to better distinguish the shared and unknown classes, we further present a cluster-level strategy named Mutual-Voting Decision (MVD) to extract robust consensus knowledge across peer classes from both source and target domains. Extensive experiments on three benchmark datasets demonstrate that our method achieves comparable performance for our UFDA scenario with much fewer assumptions, compared to previous methodologies with comprehensive additional assumptions.

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IS Journal 2020 Journal Article

SMSS: Secure Member Selection Strategy in Federated Learning

Kun Zhao
Wei Xi
Zhi Wang
Jizhong Zhao
Ruimeng Wang
Zhiping Jiang

Data security and user privacy-issue have become an important field. As federated learning (FL) could solve the problems from data security and privacy-issue, it starts to be applied in many different applied machine learning tasks. However, FL does not verify the quality of the data from different parties in the system. Hence, the low-quality datasets with fewer common entities can be cotrained with others. This could result in a huge amount of computing-resources waste, and the attack on the FL model from malicious clients as federal members. To solve this problem, this article proposes a secure member selection strategy (SMSS), which can evaluate the data qualities of members before training. With SMSS, only datasets share more common entities than a certain threshold can be selected for learning, whereas malicious clients with fewer common objects cannot acquire any information about the model. This article implements SMSS, and evaluate its performance via several extensive experiments. Experimental results demonstrate that SMSS is safe, efficient, and effective.

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