Wei Qian

AAAI Conference 2026 Conference Paper

Towards Benchmarking Privacy Vulnerabilities in Selective Forgetting with Large Language Models

• Wei Qian
• Chenxu Zhao
• Yangyi Li
• Mengdi Huai

The rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledgeable learning systems. As these systems are increasingly deployed in critical areas, ensuring their privacy and alignment with human values is paramount. Recently, selective forgetting (also known as machine unlearning) has shown promise for privacy and data removal tasks, and has emerged as a transformative paradigm shift in the field of AI. It refers to the ability of a model to selectively erase the influence of previously seen data, which is especially important for compliance with modern data protection regulations and for aligning models with human values. Despite its promise, selective forgetting raises significant privacy concerns, especially when the data involved come from sensitive domains. While new unlearning-induced privacy attacks are continuously proposed, each is shown to outperform its predecessors using different experimental settings, which can lead to overly optimistic and potentially unfair assessments that may disproportionately favor one particular attack over the others. In this work, we present the first comprehensive benchmark for evaluating privacy vulnerabilities in selective forgetting. We extensively investigate privacy vulnerabilities of machine unlearning techniques and benchmark privacy leakage across a wide range of victim data, state-of-the-art unlearning privacy attacks, unlearning methods, and model architectures. We systematically evaluate and identify critical factors related to unlearning-induced privacy leakage. With our novel insights, we aim to provide a standardized tool for practitioners seeking to deploy customized unlearning applications with faithful privacy assessments.

JBHI Journal 2025 Journal Article

CoDPN: An Unsupervised Collaborative Dual-path Network For Contactless Remote Physiological Measurement

• Yimou Lv
• Xinyuan Jiao
• Lin Qi
• Qi Zhang
• Lisheng Xu
• Wei Qian

Remote photoplethysmography (rPPG) provides a convenient solution for contactless physiological measurement, and is a promising technique for daily health monitoring and clinical application. However, traditional supervised methods rely heavily on labeled data, incurring substantial annotation costs. Current unsupervised contrastive learning methods encounter challenges under challenging conditions, including variations in illumination and motion of the head or limbs. In this paper, we propose a novel unsupervised end-to-end framework, called CoDPN for physiological measurement. CoDPN employs a dual-path architecture consisting of the Short-term Information Extraction Path (SIEP) and Long-term Information Extraction Path (LIEP), which capture short-term contextual relevance and long-term periodic dependence of rPPG signals, respectively. Next, we propose a collaborative learning strategy to integrate the latent features from both the SIEP and LIEP, facilitating the exchange of complementary information. Furthermore, our unsupervised learning strategy leverages rPPG features in both the frequency and time domains, guiding the CoDPN to extract rPPG signals consistent with physiological information while reducing dependence on labeled datasets. We conduct extensive experiments on three benchmark datasets (UBFC-rPPG, PURE, and UBFC-phys) to implement physiological measurement, including heart rate (HR), heart rate variability (HRV) and respiratory frequency (RF). The experimental results demonstrate that our CoDPN outperforms other state-of-the-art methods under complex conditions.

AAAI Conference 2025 Conference Paper

Dense Audio-Visual Event Localization Under Cross-Modal Consistency and Multi-Temporal Granularity Collaboration

• Ziheng Zhou
• Jinxing Zhou
• Wei Qian
• Shengeng Tang
• Xiaojun Chang
• Dan Guo

In the field of audio-visual learning, most research tasks focus exclusively on short videos. This paper focuses on the more practical Dense Audio-Visual Event Localization (DAVEL) task, advancing audio-visual scene understanding for longer, untrimmed videos. This task seeks to identify and temporally pinpoint all events simultaneously occurring in both audio and visual streams. Typically, each video encompasses dense events of multiple classes, which may overlap on the timeline, each exhibiting varied durations. Given these challenges, effectively exploiting the audio-visual relations and the temporal features encoded at various granularities becomes crucial. To address these challenges, we introduce a novel CCNet, comprising two core modules: the Cross-Modal Consistency Collaboration (CMCC) and the Multi-Temporal Granularity Collaboration (MTGC). Specifically, the CMCC module contains two branches: a cross-modal interaction branch and a temporal consistency-gated branch. The former branch facilitates the aggregation of consistent event semantics across modalities through the encoding of audio-visual relations, while the latter branch guides one modality's focus to pivotal event-relevant temporal areas as discerned in the other modality. The MTGC module includes a coarse-to-fine collaboration block and a fine-to-coarse collaboration block, providing bidirectional support among coarse- and fine-grained temporal features. Extensive experiments on the UnAV-100 dataset validate our module design, resulting in a new state-of-the-art performance in dense audio-visual event localization.

AAAI Conference 2025 Short Paper

Neuron Explanations for Conformal Prediction (Student Abstract)

• Divya Lidder
• Kathryn Morse
• Bridget Sullivan
• Wei Qian
• Chenglin Miao
• Mengdi Huai

Conformal prediction (CP) has gained prominence as a popular technique for uncertainty quantification in deep neural networks (DNNs), providing statistically rigorous uncertainty sets. However, existing CP methods fail to clarify the origins of predictive uncertainties. While neuron-level interpretability has been effective in revealing the internal mechanisms of DNNs, explaining CP at the neuron level remains unexplored. Nonetheless, generating neuron explanations for CP is challenging due to the discrete and non-differentiable characteristics of CP, and the labor-intensive process of semantic annotation. To address these limitations, this paper proposes a novel neuron explanation approach for CP by identifying neurons crucial for understanding predictive uncertainties and automatically generating semantic explanations. The effectiveness of the proposed method is validated through both qualitative and quantitative experiments.

AAAI Conference 2025 Conference Paper

PhysDiff: Physiology-based Dynamicity Disentangled Diffusion Model for Remote Physiological Measurement

• Wei Qian
• Gaoji Su
• Dan Guo
• Jinxing Zhou
• Xiaobai Li
• Bin Hu
• Shengeng Tang
• Meng Wang

Recent works on remote PhotoPlethysmoGraphy (rPPG) estimation typically use techniques like CNNs and Transformers to encode implicit features from facial videos for prediction. These methods learn to directly map facial videos to the static values of rPPG signals, overlooking the inherent dynamic characteristics of rPPG sequence. Moreover, the rPPG signal is extremely weak and highly susceptible to interference from various sources of noise, including illumination conditions, head movements, and variations in skin tone. To address these limitations, we propose a Physiology-based dynamicity disentangled diffusion (PhysDiff) model particularly designed for robust rPPG estimation. PhysDiff leverages the diffusion model to learn the distribution of quasi-periodic rPPG signal and uses a dynamicity disentanglement strategy to capture two dynamic characteristics in temporal rPPG signal, i.e., trend and amplitude. This disentanglement is motivated by the underlying dynamic physiological processes of vasodilation and vasoconstriction, ensuring a more precise representation of the rPPG signal. The disentangled components are then used as pivotal conditions in the proposed spatial-temporal hybrid denoiser for rPPG reconstruction. Besides, we introduce a periodicity-based multi-hypothesis selection strategy in model inference, which compares the natural periodicity of multiple generated rPPG hypotheses and selects the most favorable one as the final prediction. Extensive experiments on four datasets demonstrate that our PhysDiff significantly outperforms prior methods on both intra-dataset and cross-dataset testing.

AAAI Conference 2024 Short Paper

Automated Natural Language Explanation of Deep Visual Neurons with Large Models (Student Abstract)

• Chenxu Zhao
• Wei Qian
• Yucheng Shi
• Mengdi Huai
• Ninghao Liu

Interpreting deep neural networks through examining neurons offers distinct advantages when it comes to exploring the inner workings of Deep Neural Networks. Previous research has indicated that specific neurons within deep vision networks possess semantic meaning and play pivotal roles in model performance. Nonetheless, the current methods for generating neuron semantics heavily rely on human intervention, which hampers their scalability and applicability. To address this limitation, this paper proposes a novel post-hoc framework for generating semantic explanations of neurons with large foundation models, without requiring human intervention or prior knowledge. Experiments are conducted with both qualitative and quantitative analysis to verify the effectiveness of our proposed approach.

ICML Conference 2024 Conference Paper

Data Poisoning Attacks against Conformal Prediction

• Yangyi Li
• Aobo Chen
• Wei Qian
• Chenxu Zhao
• Divya Lidder
• Mengdi Huai

The efficient and theoretically sound uncertainty quantification is crucial for building trust in deep learning models. This has spurred a growing interest in conformal prediction (CP), a powerful technique that provides a model-agnostic and distribution-free method for obtaining conformal prediction sets with theoretical guarantees. However, the vulnerabilities of such CP methods with regard to dedicated data poisoning attacks have not been studied previously. To bridge this gap, for the first time, we in this paper propose a new class of black-box data poisoning attacks against CP, where the adversary aims to cause the desired manipulations of some specific examples’ prediction uncertainty results (instead of misclassifications). Additionally, we design novel optimization frameworks for our proposed attacks. Further, we conduct extensive experiments to validate the effectiveness of our attacks on various settings (e. g. , the full and split CP settings). Notably, our extensive experiments show that our attacks are more effective in manipulating uncertainty results than traditional poisoning attacks that aim at inducing misclassifications, and existing defenses against conventional attacks are ineffective against our proposed attacks.

AAAI Conference 2024 Conference Paper

Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving

• Junkai Xu
• Liang Peng
• Haoran Cheng
• Linxuan Xia
• Qi Zhou
• Dan Deng
• Wei Qian
• Wenxiao Wang

Multi-camera perception tasks have gained significant attention in the field of autonomous driving. However, existing frameworks based on Lift-Splat-Shoot (LSS) in the multi-camera setting cannot produce suitable dense 3D features due to the projection nature and uncontrollable densification process. To resolve this problem, we propose to regulate intermediate dense 3D features with the help of volume rendering. Specifically, we employ volume rendering to process the dense 3D features to obtain corresponding 2D features (e.g., depth maps, semantic maps), which are supervised by associated labels in the training. This manner regulates the generation of dense 3D features on the feature level, providing appropriate dense and unified features for multiple perception tasks. Therefore, our approach is termed Vampire, stands for ``Volume rendering As Multi-camera Perception Intermediate feature REgulator''. Experimental results on the Occ3D and nuScenes datasets demonstrate that Vampire facilitates fine-grained and appropriate extraction of dense 3D features, and is competitive with existing SOTA methods across diverse downstream perception tasks like 3D occupancy prediction, LiDAR segmentation and 3D objection detection, while utilizing moderate GPU resources. We provide a video demonstration in the supplementary materials and Codes are available at github.com/cskkxjk/Vampire.

ICML Conference 2024 Conference Paper

Rethinking Adversarial Robustness in the Context of the Right to be Forgotten

• Chenxu Zhao
• Wei Qian
• Yangyi Li
• Aobo Chen
• Mengdi Huai

The past few years have seen an intense research interest in the practical needs of the "right to be forgotten", which has motivated researchers to develop machine unlearning methods to unlearn a fraction of training data and its lineage. While existing machine unlearning methods prioritize the protection of individuals’ private data, they overlook investigating the unlearned models’ susceptibility to adversarial attacks and security breaches. In this work, we uncover a novel security vulnerability of machine unlearning based on the insight that adversarial vulnerabilities can be bolstered, especially for adversarially robust models. To exploit this observed vulnerability, we propose a novel attack called Adversarial Unlearning Attack (AdvUA), which aims to generate a small fraction of malicious unlearning requests during the unlearning process. AdvUA causes a significant reduction of adversarial robustness in the unlearned model compared to the original model, providing an entirely new capability for adversaries that is infeasible in conventional machine learning pipelines. Notably, we also show that AdvUA can effectively enhance model stealing attacks by extracting additional decision boundary information, further emphasizing the breadth and significance of our research. We also conduct both theoretical analysis and computational complexity of AdvUA. Extensive numerical studies are performed to demonstrate the effectiveness and efficiency of the proposed attack.

JBHI Journal 2024 Journal Article

ST-GAN: A Swin Transformer-Based Generative Adversarial Network for Unsupervised Domain Adaptation of Cross-Modality Cardiac Segmentation

• Yifan Zhang
• Yonghui Wang
• Lisheng Xu
• Yudong Yao
• Wei Qian
• Lin Qi

Unsupervised domain adaptation (UDA) methods have shown great potential in cross-modality medical image segmentation tasks, where target domain labels are unavailable. However, the domain shift among different image modalities remains challenging, because the conventional UDA methods are based on convolutional neural networks (CNNs), which tend to focus on the texture of images and cannot establish the global semantic relevance of features due to the locality of CNNs. This paper proposes a novel end-to-end Swin Transformer-based generative adversarial network (ST-GAN) for cross-modality cardiac segmentation. In the generator of ST-GAN, we utilize the local receptive fields of CNNs to capture spatial information and introduce the Swin Transformer to extract global semantic information, which enables the generator to better extract the domain-invariant features in UDA tasks. In addition, we design a multi-scale feature fuser to sufficiently fuse the features acquired at different stages and improve the robustness of the UDA network. We extensively evaluated our method with two cross-modality cardiac segmentation tasks on the MS-CMR 2019 dataset and the M&Ms dataset. The results of two different tasks show the validity of ST-GAN compared with the state-of-the-art cross-modality cardiac image segmentation methods.

AAAI Conference 2024 Conference Paper

Towards Modeling Uncertainties of Self-Explaining Neural Networks via Conformal Prediction

• Wei Qian
• Chenxu Zhao
• Yangyi Li
• Fenglong Ma
• Chao Zhang
• Mengdi Huai

Despite the recent progress in deep neural networks (DNNs), it remains challenging to explain the predictions made by DNNs. Existing explanation methods for DNNs mainly focus on post-hoc explanations where another explanatory model is employed to provide explanations. The fact that post-hoc methods can fail to reveal the actual original reasoning process of DNNs raises the need to build DNNs with built-in interpretability. Motivated by this, many self-explaining neural networks have been proposed to generate not only accurate predictions but also clear and intuitive insights into why a particular decision was made. However, existing self-explaining networks are limited in providing distribution-free uncertainty quantification for the two simultaneously generated prediction outcomes (i.e., a sample's final prediction and its corresponding explanations for interpreting that prediction). Importantly, they also fail to establish a connection between the confidence values assigned to the generated explanations in the interpretation layer and those allocated to the final predictions in the ultimate prediction layer. To tackle the aforementioned challenges, in this paper, we design a novel uncertainty modeling framework for self-explaining networks, which not only demonstrates strong distribution-free uncertainty modeling performance for the generated explanations in the interpretation layer but also excels in producing efficient and effective prediction sets for the final predictions based on the informative high-level basis explanations. We perform the theoretical analysis for the proposed framework. Extensive experimental evaluation demonstrates the effectiveness of the proposed uncertainty framework.

NeurIPS Conference 2023 Conference Paper

Static and Sequential Malicious Attacks in the Context of Selective Forgetting

• Chenxu Zhao
• Wei Qian
• Rex Ying
• Mengdi Huai

With the growing demand for the right to be forgotten, there is an increasing need for machine learning models to forget sensitive data and its impact. To address this, the paradigm of selective forgetting (a. k. a machine unlearning) has been extensively studied, which aims to remove the impact of requested data from a well-trained model without retraining from scratch. Despite its significant success, limited attention has been given to the security vulnerabilities of the unlearning system concerning malicious data update requests. Motivated by this, in this paper, we explore the possibility and feasibility of malicious data update requests during the unlearning process. Specifically, we first propose a new class of malicious selective forgetting attacks, which involves a static scenario where all the malicious data update requests are provided by the adversary at once. Additionally, considering the sequential setting where the data update requests arrive sequentially, we also design a novel framework for sequential forgetting attacks, which is formulated as a stochastic optimal control problem. We also propose novel optimization algorithms that can find the effective malicious data update requests. We perform theoretical analyses for the proposed selective forgetting attacks, and extensive experimental results validate the effectiveness of our proposed selective forgetting attacks. The source code is available in the supplementary material.

AIIM Journal 2023 Journal Article

Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images

• Yanan Wu
• Shuiqing Zhao
• Shouliang Qi
• Jie Feng
• Haowen Pang
• Runsheng Chang
• Long Bai
• Mengqi Li

Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD. We propose a two-stage framework with a novel 3D contextual transformer for segmenting the overall airway and small airway branches using CT images. The method consists of two training stages sharing the same modified 3D U-Net network. The novel 3D contextual transformer block is integrated into both the encoder and decoder path of the network to effectively capture contextual and long-range information. In the first training stage, the proposed network segments the overall airway with the overall airway mask. To improve the performance of the segmentation result, we generate the intrapulmonary airway branch label, and train the network to focus on producing small airway branches in the second training stage. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analyses demonstrate that our proposed method extracts significantly more branches and longer lengths of the airway tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https: //github. com/zhaozsq/airway_segmentation.

YNICL Journal 2020 Journal Article

Increased thalamic volume and decreased thalamo-precuneus functional connectivity are associated with smoking relapse

• Chao Wang
• Shuyue Wang
• Zhujing Shen
• Wei Qian
• Yeerfan Jiaerken
• Xiao Luo
• Kaicheng Li
• Qingze Zeng

The thalamus, with the highest density of nicotinic acetylcholine receptor (nAChR) in the brain, plays a central role in thalamo-cortical circuits that are implicated in nicotine addiction. However, little is known about whether the thalamo-cortical circuits are potentially predictive of smoking relapse. In the current study, a total of 125 participants (84 treatment-seeking male smokers and 41 age-matched male nonsmokers) were recruited. Structural and functional magnetic resonance images (MRI) were acquired from all participants. After a 12-week smoking cessation treatment with varenicline, the smokers were then divided into relapsers (n = 54) and nonrelapsers (n = 30). Then, we compared thalamic volume and seed-based thalamo-cortical resting state functional connectivity (rsFC) prior to the cessation treatment among relapsers, nonrelapsers and nonsmokers to investigate the associations between thalamic structure/function and smoking relapse. Increased thalamic volume was detected in smokers relative to nonsmokers, and in relapsers relative to nonrelapsers, especially on the left side. Moreover, decreased left thalamo-precuneus rsFC was detected in relapsers relative to nonrelapsers. Additionally, a logistic regression analysis showed that the thalamic volume and thalamo-precuneus rsFC predicted smoking relapse with an accuracy of 75.7%. These novel findings indicate that increased thalamic volume and decreased thalamo-precuneus rsFC are associated with smoking relapse, and these thalamic measures may be used to predict treatment efficacy of nicotine addiction and serve as a potential biomarker for personalized medicine.

NeurIPS Conference 2019 Conference Paper

Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities

• Wei Qian
• Yuqian Zhang
• Yudong Chen

This work studies the location estimation problem for a mixture of two rotation invariant log-concave densities. We demonstrate that Least Squares EM, a variant of the EM algorithm, converges to the true location parameter from a randomly initialized point. Moreover, we establish the explicit convergence rates and sample complexity bounds, revealing their dependence on the signal-to-noise ratio and the tail property of the log-concave distributions. Our analysis generalizes previous techniques for proving the convergence results of Gaussian mixtures, and highlights that an angle-decreasing property is sufficient for establishing global convergence for Least Squares EM.

JBHI Journal 2018 Journal Article

An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network

• Hongyang Jiang
• He Ma
• Wei Qian
• Mengdi Gao
• Yan Li

High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer-aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography image transformation, the lung nodule segmentation, and the feature extraction, to construct a whole CADe system. It is difficult for these schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multigroup patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80. 06% with 4. 7 false positives per scan and the sensitivity of 94% with 15. 1 false positives per scan. The results demonstrate that the multigroup patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.

IJCAI Conference 2018 Conference Paper

Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism

• Wei Qian
• Cong Fu
• Yu Zhu
• Deng Cai
• Xiaofei He

Knowledge graph embedding is an essential problem in knowledge extraction. Recently, translation based embedding models (e. g. , TransE) have received increasingly attentions. These methods try to interpret the relations among entities as translations from head entity to tail entity and achieve promising performance on knowledge graph completion. Previous researchers attempt to transform the entity embedding concerning the given relation for distinguishability. Also, they naturally think the relation-related transforming should reflect attention mechanism, which means it should focus on only a part of the attributes. However, we found previous methods are failed with creating attention mechanism, and the reason is that they ignore the hierarchical routine of human cognition. When predicting whether a relation holds between two entities, people first check the category of entities, then they focus on fined-grained relation-related attributes to make the decision. In other words, the attention should take effect on entities filtered by the right category. In this paper, we propose a novel knowledge graph embedding method named TransAt to learn the translation based embedding, relation-related categories of entities and relation-related attention simultaneously. Extensive experiments show that our approach outperforms state-of-the-art methods significantly on public datasets, and our method can learn the true attention varying among relations.

JMLR Journal 2016 Journal Article

Kernel Estimation and Model Combination in A Bandit Problem with Covariates

• Wei Qian
• Yuhong Yang

Multi-armed bandit problem is an important optimization game that requires an exploration-exploitation tradeoff to achieve optimal total reward. Motivated from industrial applications such as online advertising and clinical research, we consider a setting where the rewards of bandit machines are associated with covariates, and the accurate estimation of the corresponding mean reward functions plays an important role in the performance of allocation rules. Under a flexible problem setup, we establish asymptotic strong consistency and perform a finite- time regret analysis for a sequential randomized allocation strategy based on kernel estimation. In addition, since many nonparametric and parametric methods in supervised learning may be applied to estimating the mean reward functions but guidance on how to choose among them is generally unavailable, we propose a model combining allocation strategy for adaptive performance. Simulations and a real data evaluation are conducted to illustrate the performance of the proposed allocation strategy. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

IJCAI Conference 2016 Conference Paper

Non-Negative Matrix Factorization with Sinkhorn Distance

• Wei Qian
• Bin Hong
• Deng Cai
• Xiaofei He
• Xuelong Li

Non-negative Matrix Factorization (NMF) has received considerable attentions in various areas for its psychological and physiological interpretation of naturally occurring data whose representation may be parts-based in the human brain. Despite its good practical performance, one shortcoming of original NMF is that it ignores intrinsic structure of data set. On one hand, samples might be on a manifold and thus one may hope that geometric information can be exploited to improve NMF's performance. On the other hand, features might correlate with each other, thus conventional L 2 distance can not well measure the distance between samples. Although some works have been proposed to solve these problems, rare connects them together. In this paper, we propose a novel method that exploits knowledge in both data manifold and features correlation. We adopt an approximation of Earth Mover's Distance (EMD) as metric and add a graph regularized term based on EMD to NMF. Furthermore, we propose an efficient multiplicative iteration algorithm to solve it. Our empirical study shows the encouraging results of the proposed algorithm comparing with other NMF methods.

YNIMG Journal 2010 Journal Article

Imaging the cannabinoid CB1 receptor in humans with [11C]OMAR: Test–retest reproducibility and gender differences

• Marc D. Normandin
• Ming-Qiang Zheng
• Jim R. Ropchan
• David Labaree
• Soheila Najafzadeh
• Rachel Hull
• Wei Qian
• Shannan Henry