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Zhenwen Ren

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16 papers

2 author rows

AAAI Conference 2026 Conference Paper

Neural Collapse Priors Driven Trust Semi-Supervised Multi-View Classification

Taotao Guo
Honglin Yuan
Xujian Zhao
Yuan Sun
Dongliang Wang
Zhenwen Ren
Xingfeng Li

In semi‑supervised multi‑view classification (SMVC), scarce labels and noisy unlabeled data impair feature aggregation and compromise prediction reliability, while existing methods lack principled guidance and interpretability. To overcome these limitations, we propose a novel unified SMVC framework, Neural Collapse Priors Driven Trust Semi-Supervised Multi-View Classification (NCPD-TSMVC), building upon neural collapse–derived prototype priors and evidential opinion fusion. Concretely, we rigorously prove under neural collapse theory that normalized classifier weights from the labeled‑data pre‑training stage coincide with class centroids in feature space, conferring maximal inter‑class separation and optimal within‑class compactness. These prototype priors permeate the entire learning pipeline, calibrating the representation learning of unlabeled samples to obtain highly discriminative embeddings. Simultaneously, our evidential learning module quantifies epistemic uncertainty and fuses view‑level opinions at the evidence level, yielding robust and transparent decision making. Extensive evaluations across diverse benchmarks demonstrate that NCPD‑TSMVC surpasses state‑of‑the‑art SMVC approaches in performance, robustness and interpretability.

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AAAI Conference 2026 Conference Paper

Revisiting Network Inertia: Dynamic Inertia Inhibition Coupled Multidimensional Periodicity for Infrared and Visible Image Fusion

Yufeng Chen
Yuan Sun
Hao Pan
Xujian Zhao
Jian Dai
Zhenwen Ren
Xingfeng Li

Infrared and visible image fusion (IVIF) technology has become a frontier of great interest due to the ability to integrate information from multiple sources. However, the progressive slowdown of weight updates in deep networks (i.e., “network laziness” phenomenon), makes existing methods far from realizing the full characterization potential. To this end, we propose a lightweight fusion method for IVIF, Anti-Inert Dynamic Fusion (AIDFusion), to fully utilize the potential of the network at all levels. Specifically, by progressively regulating the collaborative Learning process of multi-level prediction in the network, Dynamic Inertia Inhibition Learning Strategy (DIILS) is proposed to adaptively and efficiently inhibit inertia accumulation. Subsequently, to deeply explore the representation potential while breaking through the performance threshold, lightweight Multi-dimensional modulation fusion module (MMFM) is specifically proposed to capture comprehensive multi-view and multi-scale features efficiently. Finally, considering the semantic bias between the prediction maps of DIILS and the fusion feature of MMFM, Fourier Analysis Convolution (FAConv) is designed in feature recovery as a bridge between prediction and fusion to accomplish the implicit periodic modeling. Based on the above study, extensive experiments on three public IVIF datasets demonstrate the dual advantages of AIDFusion in terms of fusion performance and computational overhead compared to state-of-the-art baseline methods.

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ICML Conference 2025 Conference Paper

CoPINN: Cognitive Physics-Informed Neural Networks

Siyuan Duan
Wenyuan Wu
Peng Hu 0002
Zhenwen Ren
Dezhong Peng
Yuan Sun 0016

Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points indiscriminately, especially ones in the physical boundaries. This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physical Informed Neural Network (CoPINN) that imitates the human cognitive learning manner from easy to hard. Specifically, we first employ separable subnetworks to encode independent one-dimensional coordinates and apply an aggregation scheme to generate multi-dimensional predicted physical variables. Then, during the training phase, we dynamically evaluate the difficulty of each sample according to the gradient of the PDE residuals. Finally, we propose a cognitive training scheduler to progressively optimize the entire sampling regions from easy to hard, thereby embracing robustness and generalization against predicting physical boundary regions. Extensive experiments demonstrate that our CoPINN achieves state-of-the-art performance, particularly significantly reducing prediction errors in stubborn regions.

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ICML Conference 2025 Conference Paper

Deep Streaming View Clustering

Honglin Yuan
Xingfeng Li 0004
Jian Dai 0002
Xiaojian You
Yuan Sun 0016
Zhenwen Ren

Existing deep multi-view clustering methods have demonstrated excellent performance, which addressing issues such as missing views and view noise. But almost all existing methods are within a static framework, which assumes that all views have already been collected. However, in practical scenarios, new views are continuously collected over time, which forms the stream of views. Additionally, there exists the data imbalance of quality and distribution between different view streams, i. e. , concept drift problem. To this end, we propose a novel Deep Streaming View Clustering (DSVC) method, which mitigates the impact of concept drift on streaming view clustering. Specifically, DSVC consists of a knowledge base and three core modules. Through the knowledge aggregation learning module, DSVC extracts representative features and prototype knowledge from the new view. Subsequently, the distribution consistency learning module aligns the prototype knowledge from the current view with the historical knowledge distribution to mitigate the impact of concept drift. Then, the knowledge guidance learning module leverages the prototype knowledge to guide the data distribution and enhance the clustering structure. Finally, the prototype knowledge from the current view is updated in the knowledge base to guide the learning of subsequent views. Extensive experiments demonstrate that, even in dynamic environments, the clustering performance of DSVC outperforms 12 state-of-the-art DMVC methods under static frameworks.

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AAAI Conference 2025 Conference Paper

Noisy Label Calibration for Multi-View Classification

Shilin Xu
Yuan Sun
Xingfeng Li
Siyuan Duan
Zhenwen Ren
Zheng Liu
Dezhong Peng

In recent years, multi-view learning has aroused extensive research passion. Most existing multi-view learning methods often rely on well-annotations to improve decision accuracy. However, noise labels are ubiquitous in multi-view data due to imperfect annotations. To deal with this problem, we propose a novel noisy label calibration method (NLC) for multi-view classification to resist the negative impact of noisy labels. Specifically, to capture consensus information from multiple views, we employ max-margin rank loss to reduce the heterogeneous gap. Subsequently, we evaluate the confidence scores to enrich predictions associated with noise instances according to all reliable neighbors. Further, we propose Label Noise Detection (LND) to separate multi-view data into a clean or noisy subset, and propose Label Calibration Learning (LCL) to correct noisy instances. Finally, we adopt the cross-entropy loss to achieve multi-view classification. Extensive experiments on six datasets validate that our method outperforms eight state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

Robust Graph Contrastive Learning for Incomplete Multi-view Clustering

Deyin Zhuang
Jian Dai
Xingfeng Li
Xi Wu
Yuan Sun
Zhenwen Ren

In recent years, multi-view clustering (MVC) has become a promising approach for analyzing heterogeneous multi-source data. However, during the collection of multi-view data, factors such as environmental interference or sensor failure often lead to the loss of view sample data, resulting in incomplete multi-view clustering (IMVC). Graph contrastive IMVC has demonstrated promising performance as an effective solution, which typically utilizes in-graph instances as positive pairs and out-of-graph instances as negative pairs. However, the construction of positive and negative pairs in this paradigm inevitably leads to graph noise Correspondence (GNC). To this end, we propose a new IMVC framework, namely robust graph contrastive learning (RGCL). Specifically, RGCL first completes the missing data by using a multi-view consistency transfer relationship graph. Then, to mitigate the impact of false negative pairs from graph contrastive, we propose noise-robust graph contrastive learning to mine intra-view consistency accurately. Finally, we present cross-view graph-level alignment to fully exploit the complementary information across different views. Experimental results on the six multi-view datasets demonstrate that our RGCL exhibits superiority and effectiveness compared with 9 state-of-the-art IMVC methods. The source code is available at https: //github. com/DYZ163/RGCL. git.

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AAAI Conference 2025 Conference Paper

Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels

Ruitao Pu
Yuan Sun
Yang Qin
Zhenwen Ren
Xiaomin Song
Huiming Zheng
Dezhong Peng

Cross-modal hashing (CMH) has appeared as a popular technique for cross-modal retrieval due to its low storage cost and high computational efficiency in large-scale data. Most existing methods implicitly assume that multi-modal data is correctly labeled, which is expensive and even unattainable due to the inevitable imperfect annotations (i.e., noisy labels) in real-world scenarios. Inspired by human cognitive learning, a few methods introduce self-paced learning to gradually train the model from easy to hard samples, which is often used to mitigate the effects of feature noise or outliers. It is a less-touched problem that how to utilize SPL to alleviate the misleading of noisy labels on the hash model. To tackle this problem, we propose a new cognitive cross-modal retrieval method called Robust Self-paced Hashing with Noisy Labels (RSHNL), which can mimic the human cognitive process to identify the noise while embracing robustness against noisy labels. Specifically, we first propose a contrastive hashing learning (CHL) scheme to improve multi-modal consistency, thereby reducing the inherent semantic gap. Afterward, we propose center aggregation learning (CAL) to mitigate the intra-class variations. Finally, we propose Noise-tolerance Self-paced Hashing (NSH) that dynamically estimates the learning difficulty for each instance and distinguishes noisy labels through the difficulty level. For all estimated clean pairs, we further adopt a self-paced regularizer to gradually learn hash codes from easy to hard. Extensive experiments demonstrate that the proposed RSHNL performs remarkably well over the state-of-the-art CMH methods.

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ICML Conference 2025 Conference Paper

SHE: Streaming-media Hashing Retrieval

Ruitao Pu
Yang Qin
Xiaomin Song
Dezhong Peng
Zhenwen Ren
Yuan Sun 0016

Recently, numerous cross-modal hashing (CMH) methods have been proposed, yielding remarkable progress. As a static learning paradigm, existing CMH methods often implicitly assume that all modalities are prepared before processing. However, in practice applications (such as multi-modal medical diagnosis), it is very challenging to collect paired multi-modal data simultaneously. Specifically, they are collected chronologically, forming streaming-media data (SMA). To handle this, all previous CMH methods require retraining on data from all modalities, which inevitably limits the scalability and flexibility of the model. In this paper, we propose a novel CMH paradigm named Streaming-media Hashing rEtrieval (SHE) that enables parallel training of each modality. Specifically, we first propose a knowledge library mining module (KLM) that extracts a prototype knowledge library for each modality, thereby revealing the commonality distribution of the instances from each modality. Then, we propose a knowledge library transfer module (KLT) that updates and aligns the new knowledge by utilizing the historical knowledge library, ensuring semantic consistency. Finally, to enhance intra-class semantic relevance and inter-class semantic disparity, we develop a discriminative hashing learning module (DHL). Comprehensive experiments on four benchmark datasets demonstrate the superiority of our SHE compared to 14 competitors.

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AAAI Conference 2025 Conference Paper

TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering

Zhongwen Wang
Xingfeng Li
Yinghui Sun
Quansen Sun
Yuan Sun
Han Ling
Jian Dai
Zhenwen Ren

In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among multi-view data and higher-order cooperative relationships during projection, negatively impacting the quality of hash representation in low-dimensional spaces, clustering performance, and sensitivity to noise. To address this issue, we propose a novel approach named Tensor-Interacted Projection and Cooperative Hashing for Multi-View Clustering(TPCH). TPCH stacks multiple projection matrices into a tensor, taking into account the synergies and communications during the projection process. By capturing higher-order multi-view information through dual projection and Hamming space, TPCH employs an enhanced tensor nuclear norm to learn more compact and distinguishable hash representations, promoting communication within and between views. Experimental results demonstrate that this refined method significantly outperforms state-of-the-art methods in clustering on five large-scale multi-view datasets. Moreover, in terms of CPU time, TPCH achieves substantial acceleration compared to the most advanced current methods.

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AAAI Conference 2024 Conference Paper

Dual Self-Paced Cross-Modal Hashing

Yuan Sun
Jian Dai
Zhenwen Ren
Yingke Chen
Dezhong Peng
Peng Hu

Cross-modal hashing~(CMH) is an efficient technique to retrieve relevant data across different modalities, such as images, texts, and videos, which has attracted more and more attention due to its low storage cost and fast query speed. Although existing CMH methods achieve remarkable processes, almost all of them treat all samples of varying difficulty levels without discrimination, thus leaving them vulnerable to noise or outliers. Based on this observation, we reveal and study dual difficulty levels implied in cross-modal hashing learning, \ie instance-level and feature-level difficulty. To address this problem, we propose a novel Dual Self-Paced Cross-Modal Hashing (DSCMH) that mimics human cognitive learning to learn hashing from ``easy'' to ``hard'' in both instance and feature levels, thereby embracing robustness against noise/outliers. Specifically, our DSCMH assigns weights to each instance and feature to measure their difficulty or reliability, and then uses these weights to automatically filter out the noisy and irrelevant data points in the original space. By gradually increasing the weights during training, our method can focus on more instances and features from ``easy'' to ``hard'' in training, thus mitigating the adverse effects of noise or outliers. Extensive experiments are conducted on three widely-used benchmark datasets to demonstrate the effectiveness and robustness of the proposed DSCMH over 12 state-of-the-art CMH methods.

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IJCAI Conference 2024 Conference Paper

Dual Semantic Fusion Hashing for Multi-Label Cross-Modal Retrieval

Kaiming Liu
Yunhong Gong
Yu Cao
Zhenwen Ren
Dezhong Peng
Yuan Sun

Cross-modal hashing (CMH) has been widely used for multi-modal retrieval tasks due to its low storage cost and fast query speed. Although existing CMH methods achieve promising performance, most of them mainly rely on coarse-grained supervision information (\ie pairwise similarity matrix) to measure the semantic similarities between all instances, ignoring the impact of multi-label distribution. To address this issue, we construct fine-grained semantic similarity to explore the cluster-level semantic relationships between multi-label data, and propose a new dual semantic fusion hashing (DSFH) for multi-label cross-modal retrieval. Specifically, we first learn the modal-specific representation and consensus hash codes, thereby merging the specificity with consistency. Then, we fuse the coarse-grained and fine-grained semantics to mine multiple-level semantic relationships, thereby enhancing hash codes discrimination. Extensive experiments on three benchmarks demonstrate the superior performance of our DSFH compared with 16 state-of-the-art methods.

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IJCAI Conference 2024 Conference Paper

Fast Unpaired Multi-view Clustering

Xingfeng Li
Yuangang Pan
Yinghui Sun
Quansen Sun
Ivor Tsang
Zhenwen Ren

Anchor based pair-wised multi-view clustering often assumes multi-view data are paired, and has demonstrated significant advancements in recent years. However, this presumption is easily violated, and data is commonly unpaired fully in practical applications due to the influence of data collection and storage processes. Addressing unpaired large-scale multi-view data through anchor learning remains a research gap. The absence of pairing in multi-view data disrupts the consistency and complementarity of multiple views, posing significant challenges in learning powerful and meaningful anchors and bipartite graphs from unpaired multi-view data. To tackle this challenge, this study proposes a novel Fast Unpaired Multi-view Clustering (FUMC) framework for fully unpaired large-scale multi-view data. Specifically, FUMC first designs an inverse local manifold learning paradigm to guide the learned anchors for effective pairing and balancing, ensuring alignment, fairness, and power in unpaired multi-view data. Meanwhile, a novel bipartite graph matching framework is developed to align unpaired bipartite graphs, creating a consistent bipartite graph from unpaired multi-view data. The efficacy, efficiency, and superiority of our FUMC are corroborated through extensive evaluations on numerous benchmark datasets with shallow and deep SOTA methods.

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EAAI Journal 2024 Journal Article

Lightweight face mask detection algorithm with attention mechanism

Yanhua Shao
Jiajia Ning
Huicao Shao
Duo Zhang
Hongyu Chu
Zhenwen Ren

Lightweight computer vision models with a better balance of speed and accuracy are critical for intelligent system devices. Wearing a mask correctly can effectively prevent the spread of respiratory diseases. However, most of the deep learning-based face mask detection(FMD) algorithms are too large and have limited accuracy, limiting their deployment in real-world application scenarios. Therefore, this paper proposes a novel object detector, lightweight FMD through You Only Look Once (LFMD-YOLO), which can achieve an excellent balance of precision and speed. First, the C3E(CSP Bottleneck with 3 convolutions-ECA) module and MECAPF(max-pooling ECA pyramid-fast) module based on the effective attention mechanism (ECA) are designed to enrich channel information. Further, a new backbone network combining C3E and MECAPF modules is proposed. Second, the weighted bidirectional feature pyramid network based on the C3E module (E-BiFPN) is designed as the feature fusion neck, making full use of multi-scale features to mine more local information and enhancing the representation of small objects of face masks. Third, the model performance is further enhanced by adding detection heads and improving intersection over union (IoU), etc. Finally, two public databases were benchmarked and compared with sixteen other state-of-the-art detection algorithms. The experimental results show that the proposed LFMD-YOLO achieves higher detection accuracy with mAPs of 68. 7% and 60. 1%, respectively, while having lower parameters and GFLOPs. In addition, we conducted experiments on the Jetson NVIDIA TX2 to further verify the effectiveness of the LFMD-YOLO. The code is available at https: //github. com/ddhappy18/LFMD-YOLO.

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EAAI Journal 2024 Journal Article

Multiple kernel graph clustering with shifted Laplacian reconstruction

Yanglei Hou
Jiali You
Chunyu Zhao
Jian Dai
Xiaojian You
Zhenwen Ren

Multiple kernel graph clustering methods have gained favor among researchers for their ability to combine the strengths of graph learning and multiple kernel methods. We propose a novel method named multiple kernel graph clustering with shifted Laplacian reconstruction (SLR-MKGC). Specifically, the kernel matrix is regarded as an affinity graph, and then the graph is transformed to a shifted Laplacian matrix. By decomposing the shifted Laplacian matrix, the latent data representation is obtained, simultaneously preserving the main energy and clustering information. As a result, the effects of noise and redundancy are reduced and the quality of the raw data are improved. Then, the obtained representation is used to reconstruct a final affinity graph with a desired block diagonal structure. Further, we conduct extensive experiments on seven benchmark datasets and compare nine state-of-the-art clustering methods. Experimental results show that SLR-MKGC exhibits excellent performance on most datasets. For example, compared to other state-of-the-art methods, SLR-MKGC achieves a performance improvement of 5. 13% on the clustering accuracy term on the BBCSport2 dataset, demonstrating the promise of SLR-MKGC. The source code is available at https: //github. com/dililidida/SLR-MKGC.

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AAAI Conference 2023 Conference Paper

Priori Anchor Labels Supervised Scalable Multi-View Bipartite Graph Clustering

Jiali You
Zhenwen Ren
Xiaojian You
Haoran Li
Yuancheng Yao

Although multi-view clustering (MVC) has achieved remarkable performance by integrating the complementary information of views, it is inefficient when facing scalable data. Proverbially, anchor strategy can mitigate such a challenge a certain extent. However, the unsupervised dynamic strategy usually cannot obtain the optimal anchors for MVC. The main reasons are that it does not consider the fairness of different views and lacks the priori supervised guidance. To completely solve these problems, we first propose the priori anchor graph regularization (PAGG) for scalable multi-view bipartite graph clustering, dubbed as SMGC method. Specifically, SMGC learns a few representative consensus anchors to simulate the numerous view data well, and constructs a bipartite graph to bridge the affinities between the anchors and original data points. In order to largely improve the quality of anchors, PAGG predefines prior anchor labels to constrain the anchors with discriminative cluster structure and fair view allocation, such that a better bipartite graph can be obtained for fast clustering. Experimentally, abundant of experiments are accomplished on six scalable benchmark datasets, and the experimental results fully demonstrate the effectiveness and efficiency of our SMGC.

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AAAI Conference 2021 Conference Paper

Multiple Kernel Clustering with Kernel k-Means Coupled Graph Tensor Learning

Zhenwen Ren
Quansen Sun
Dong Wei

Kernel k-means (KKM) and spectral clustering (SC) are two basic methods used for multiple kernel clustering (MKC), which have both been widely used to identify clusters that are non-linearly separable. However, both of them have their own shortcomings: 1) the KKM-based methods usually focus on learning a discrete clustering indicator matrix via a combined consensus kernel, but cannot exploit the high-order affinities of all pre-defined base kernels; and 2) the SC-based methods require a robust and meaningful affinity graph in kernel space as input in order to form clusters with desired clustering structure. In this paper, a novel method, kernel k-means coupled graph tensor (KCGT), is proposed to graciously couple KKM and SC for seizing their merits and evading their demerits simultaneously. In specific, we innovatively develop a new graph learning paradigm by leveraging an explicit theoretical connection between clustering indicator matrix and affinity graph, such that the affinity graph propagated from KKM enjoys the valuable block diagonal and sparse property. Then, by using this graph learning paradigm, base kernels can produce multiple candidate affinity graphs, which are stacked into a low-rank graph tensor for capturing the highorder affinity of all these graphs. After that, by averaging all the frontal slices of the tensor, a high-quality affinity graph is obtained. Extensive experiments have shown the superiority of KCGT compared with the state-of-the-art MKC methods.

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