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Chang Tang

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27 papers

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AAAI Conference 2026 Conference Paper

CLUENet: Cluster Attention Makes Neural Networks Have Eyes

Xiangshuai Song
Jun-Jie Huang
Tianrui Liu
Ke Liang
Chang Tang

Despite the success of convolution- and attention-based models in vision tasks, their rigid receptive fields and complex architectures limit their ability to model irregular spatial patterns and hinder interpretability, thereby posing challenges for tasks requiring high model transparency. Clustering paradigms offer promising interpretability and flexible semantic modeling, but suffer from limited accuracy, low efficiency, and gradient vanishing during training. To address these issues, we propose the CLUster attEntion Network (CLUENet), a transparent deep architecture for visual semantic understanding. Specifically, we introduce three key innovations, including (i) a Global and Soft Feature Aggregation with a Temperature-Scaled Cosine Attention for capturing long-range dependencies and a Gated Fusion Mechanism for enhanced local modeling, (ii) Hard and Shared Feature Dispatching, and (iii) an Improved Cluster Pooling Block. These enhancements significantly improve both classification performance and visual interpretability. Experiments on CIFAR-100 and Mini-ImageNet demonstrate that CLUENet outperforms existing clustering methods and mainstream visual models, offering a compelling balance of accuracy, efficiency, and transparency.

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JBHI Journal 2026 Journal Article

Contrastive Representation Learning for Cross-Domain Blood Cell Image Classification With Denoising Mechanism

Renyu Fu
Xiao Zheng
Hua Zhou
Chengfu Ji
Sen Xiang
Guanghui Yue
Tianyi Wang
Chang Tang

Accurate identification and classification of white blood cells are essential for diagnosing hematological malignancies and analyzing blood disorders. Existing approaches predominantly leverage masked autoencoders (MAEs) to extract intrinsic blood cell features through image reconstruction as a pretext task. However, these methods encounter two critical challenges: (1) their generalization performance deteriorates under domain shifts caused by variations in staining techniques, illumination conditions, and microscope settings, and (2) the learned data distribution often deviates from the true distribution of blood cell features. To overcome these limitations, we propose CD-CBC, a novel framework for cross-domain blood cell image classification that integrates contrastive representation learning with a denoising mechanism. CD-CBC consists of two key components: a LoRA-based segmentation anything model (LoRA-SAM) and a contrastive masked autoencoder (CMAE). LoRA-SAM mitigates shortcut learning in contrastive learning by eliminating background noise and platelet interference, while CMAE captures fine-grained semantic features and models spatial relationships, enhancing cross-domain robustness. Additionally, we introduce a denoising mechanism in the latent space, which guides the model to focus on unmasked patches during reconstruction, allowing it to better capture the true distribution of blood cell features. Extensive experiments on two benchmark blood cell datasets demonstrate that CD-CBC achieves superior cross-domain performance, reaching an average accuracy of 62. 47%, which is 3. 17% higher than the current state-of-the-art, thereby confirming its strong generalization capability.

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JBHI Journal 2026 Journal Article

HCA-Net: Hierarchical Contextual Attention Network for Lightweight and Accurate Polyp Segmentation

Chengcheng Li
Huiying Xu
Xinzhong Zhu
Huiling Chen
Xinwang Liu
Yun Liu
Chang Tang
Zhendong Chen

Early detection of colorectal polyps is crucial for clinical screening and cancer prevention, where accurate and efficient automatic segmentation plays a pivotal role. However, colonoscopy images often suffer from low contrast, blurred boundaries, and scale variations, making segmentation challenging. Existing encoder-decoder networks (e. g. , U-Net) suffer from asymmetric supervision and feature redundancy, which in turn lead to semantic inconsistency and loss of fine details. While deeper or hybrid designs alleviate these issues, their high complexity and computational burden limit feasibility in real-time clinical practice. To address these challenges, we propose a lightweight segmentation framework, Hierarchical Contextual Attention Network (HCA-Net), consisting of the Redundancy-Suppressed Dual-Path Downsampling (RS-DPD) module and the Boundary-Aware Semantic Alignment Upsampling (BA-SAU) module, applied to the encoder and decoder, respectively. RS-DPD suppresses redundancy while preserving fine-grained details through a dual-path design, whereas BA-SAU leverages cross-layer contextual attention to enforce semantic consistency and enhance boundary sensitivity. Both modules are built upon our proposed Hierarchical Contextual Attention (HCA) mechanism, which combines convolutional projection with pooling-based compression to achieve efficient global modeling and accurate local boundary restoration. In addition, a composite boundary-aware loss function is designed to improve pixel-level accuracy, structural consistency, and robustness in low-contrast and boundary-ambiguous regions. Extensive experiments on public colorectal polyp datasets demonstrate that HCA-Net achieves state-of-the-art (SOTA) segmentation accuracy with significantly improved efficiency, while maintaining robustness under low-contrast and blurred-boundary conditions.

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AAAI Conference 2026 Conference Paper

PLA-MGRA: Multi-Granularity and Relation-Aware Learning for Efficient and Generalizable Protein-Ligand Binding Affinity Prediction

Shunfan Li
Jiangkai Long
Xin Zou
Chang Tang
Yuanyuan Liu
Xiao He
Xuesong Yan

Protein-Ligand Affinity (PLA) prediction quantifies the interaction strength to guide rational drug design. Existing approaches typically analyze interaction at a single granularity and overlook tightly coupled relationships between protein and ligand in both structure and functionality, consequently yielding suboptimal representations, leading to significant performance drops in real-world scenarios. To address this problem, we propose PLA-MGRA, a minimalist and effective PLA prediction framework. Specifically, PLA-MGRA captures both fine-grained atomic details and coarse grained functional semantics within the 3D structure of protein–ligand complexes, through multi-granularity learning. To further parse the coupled protein–ligand relationships, we design relation-aware learning to enhance the binding nature of representations. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple protein–ligand affinity prediction benchmarks, while also offering generalizability and interpretability.

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AAAI Conference 2026 Conference Paper

SGAT: Learning Feature Matching with Singularity-enhanced Graph Attention Network

Yizhuo Zhang
Kun Sun
Chang Tang
Yuanyuan Liu
Xin Li

The task of image feature matching aims to establish correct correspondences between images from two different views. While approaches based on attention mechanisms have demonstrated remarkable advancements in image feature matching, they still encounter substantial limitations. Specifically, current graph attention network approaches face performance bottlenecks in complex scenarios, such as low-texture regions or occlusions. This limitation stems from the self-attention mechanism, which, when lacking effective guidance, can lead to divergent attention weights or incorrect focus on regions with low discriminability, resulting in matching failures in low-texture environments. Inspired by how humans focus on distinctive regions when performing cross-view matching, we enhance attention to singular points in images that are salient, unique and have high cross-view matching potential during information aggregation, thereby improving matching capability. To realize the aforementioned strategies, we develop a novel Singularity-enhanced Graph Attention Network (SGAT). SGAT leverages Co-potentiality and Multi-Scale Singularity as prior guidance, and designs a Singularity-aware Attention mechanism and a Co-potentiality Guided Attention mechanism, specifically enhancing the perception of singularity and matching potential during feature interaction. Experimental results on multiple datasets, including ScanNet1500, demonstrate that our method outperforms current state-of-the-art sparse matching methods. In particular, the improvement is most pronounced in complex scenarios such as low-texture environments, significantly enhancing the accuracy and robustness of image matching and its downstream tasks.

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AAAI Conference 2026 Conference Paper

When Genes Speak: A Semantic-Guided Framework for Spatially Resolved Transcriptomics Data Clustering

Jiangkai Long
Yanran Zhu
Chang Tang
Kun Sun
Yuanyuan Liu
Xuesong Yan

Spatial transcriptomics enables gene expression profiling with spatial context, offering unprecedented insights into the tissue microenvironment. However, most computational models treat genes as isolated numerical features, ignoring the rich biological semantics encoded in their symbols. This prevents a truly deep understanding of critical biological characteristics. To overcome this limitation, we present SemST, a semantic-guided deep learning framework for spatial transcriptomics data clustering. SemST leverages Large Language Models (LLMs) to enable genes to "speak" through their symbolic meanings, transforming gene sets within each tissue spot into biologically informed embeddings. These embeddings are then fused with the spatial neighborhood relationships captured by Graph Neural Networks (GNNs), achieving a coherent integration of biological function and spatial structure. We further introduce the Fine-grained Semantic Modulation (FSM) module to optimally exploit these biological priors. The FSM module learns spot-specific affine transformations that empower the semantic embeddings to perform an element-wise calibration of the spatial features, thus dynamically injecting high-order biological knowledge into the spatial context. Extensive experiments on public spatial transcriptomics datasets show that SemST achieves state-of-the-art clustering performance. Crucially, the FSM module exhibits plug-and-play versatility, consistently improving the performance when integrated into other baseline methods.

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EAAI Journal 2025 Journal Article

Cancer–drug response prediction via feature aggregation and association graph learning

Kaiyi Xu
Minhui Wang
Xin Zou
Chengfu Ji
Hua Zhou
Chang Tang

Predicting cancer–drug response (CDR) not only guides the development of anticancer drugs but also provides personalized treatment plans for cancer patients, driving the advancement of precision medicine. Currently, numerous computational methods are utilized for CDR prediction and achieve great achievements. However, there are still some issues need to be addressed. From the aspect of data using, many studies primarily use single nucleotide variations (SNV) as features of cell lines, yet their acquisition from clinical data is often incomplete and inefficient, making it challenging to extend SNV-based CDR models from in vitro cell lines to clinical data. From the aspect of modeling, most works focus excessively on learning the intrinsic characteristics of drugs and cell lines, while neglecting to fully capture their interaction information and network-level knowledge. In this paper, we introduce a feature aggregation and association graph learning network (F A 2 GL) for CDR, which aggregates intrinsic and interaction feature representations of drugs and cell lines, and obtains their network-level knowledge from the association graph. In this network, F A 2 GL initially employs the self-attention block (SAB) to capture drug intrinsic features, with a focus on the relationships between substructures within the same drug, and utilizes the multilayer perceptron (MLP) to obtain cell line intrinsic features. Subsequently, it constructs the cross-attention block (CAB) to extract interaction information between drugs and cell lines, followed by the utilization of a fusion coefficient for aggregating these features. Additionally, F A 2 GL also constructs an association graph by introducing pipe nodes and similarity networks, bridging the feature gap between drugs and cell lines and enhancing predictions of similar substances, respectively. Then, a graph neural network (GNN) is applied to this graph to capture their network-level information. By integrating fine-grained and network-level features, F A 2 GL demonstrates competitiveness compared to state-of-the-art methods on Genomics of Drug Sensitivity in Cancer (GDSC) dataset which contains various cancers such as acute lymphoblastic leukemia, diffuse large B-Cell lymphoma, and multiple myeloma, while avoiding the use of large-scale SNV data. The codes and data for F A 2 GL are available at https: //github. com/Dtdtxuky/FA-2GL/tree/main.

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JBHI Journal 2025 Journal Article

DD-HGNN$^+$: Drug-Disease Association Prediction via General Hypergraph Neural Network With Hierarchical Contrastive Learning and Cross Attention Learning

Zixiao Jin
Xiao Zheng
Hua Zhou
Chengfu Ji
Sen Xiang
Chang Tang

The research on identifying drug-disease associations (DDAs) is widely used in scenarios such as drug development, clinical decision-making, and drug repurposing, holding significant biological and medical significance. Existing methods for drug-disease association prediction have achieved decent performance, they primarily rely on simplistic drug-disease association graphs or similarity graphs. These methods often struggle to capture the high-order correlations of complex multimodal data, limiting their ability to handle the complexity of data associations effectively. In addition, real drug-disease associations are highly sparse, posing a significant challenge to prediction accuracy. To tackle these issues, we propose a general hypergraph neural network framework for drug-disease association prediction based on hierarchical contrastive learning and cross-attention learning. It leverages hypergraph neural networks to learn representations of drugs and diseases carrying high-order correlations and strengthens representation quality using interactive attention learning and hierarchical contrastive learning. Meanwhile, the $\lambda$ -weighted loss function is utilized to adapt to the high sparsity property of real drug-disease associations during model training and improve prediction performance. Extensive experiments demonstrate that DD-HGNN $^+$ surpasses other state-of-the-art methods in predicting drug-disease associations and further validation through case studies on Leukemia and Colorectal Neoplasms underscores its reliability.

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ICML Conference 2025 Conference Paper

Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models

Xin Zou 0001
Yizhou Wang
Yibo Yan
Yuanhuiyi Lyu
Kening Zheng
Sirui Huang
Junkai Chen
Peijie Jiang

Despite their impressive capabilities, Multimodal Large Language Models (MLLMs) are prone to hallucinations, i. e. , the generated content that is nonsensical or unfaithful to input sources. Unlike in LLMs, hallucinations in MLLMs often stem from the sensitivity of text decoder to visual tokens, leading to a phenomenon akin to "amnesia" about visual information. To address this issue, we propose MemVR, a novel decoding paradigm inspired by common cognition: when the memory of an image seen the moment before is forgotten, people will look at it again for factual answers. Following this principle, we treat visual tokens as supplementary evidence, re-injecting them into the MLLM through Feed Forward Network (FFN) as “key-value memory” at the middle trigger layer. This look-twice mechanism occurs when the model exhibits high uncertainty during inference, effectively enhancing factual alignment. Comprehensive experimental evaluations demonstrate that MemVR significantly mitigates hallucination across various MLLMs and excels in general benchmarks without incurring additional time overhead.

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IJCAI Conference 2025 Conference Paper

LRGR: Self-Supervised Incomplete Multi-View Clustering via Local Refinement and Global Realignment

Yanwanyu Xi
Xiao Zheng
Chang Tang
Xingchen Hu
Yuanyuan Liu
Jun-Jie Huang
Xinwang Liu

Incomplete Multi-View Clustering (IMVC) aims to explore comprehensive representations from multiple views with missing samples. Recent studies have revealed that IMVC methods benefit from Graph Convolutional Network (GCN) in achieving robust feature imputation and effective representation learning. Despite these notable improvements, GCN imputation methods often cause a distribution shift between the imputed and original representations, particularly when the neighbors of the imputed nodes are assigned to different groups. Moreover, GCN learning methods tend to produce homogeneous imputed representations, which blur cluster boundaries and hinder effective discriminative clustering. To remedy these challenges, the Local Refinement and Global Realignment (LRGR) Self-supervised model is proposed for incomplete multi-view clustering, which includes two stages. In the first stage, a local imputed refinement module is designed to enhance the versatility of imputed representations through cross-view contrastive learning guided by view-specific prototypes. In the second stage, a global realignment module is introduced to achieve semantic consistency across views, alleviating distribution shifts by leveraging pseudo-labels and their corresponding confidence scores as guidance. Experiments on five widely used multi-view datasets demonstrate the competitiveness and superiority of our method compared to state-of-the-art approaches.

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NeurIPS Conference 2025 Conference Paper

Scalable Cross-View Sample Alignment for Multi-View Clustering with View Structure Similarity

Jun Wang
Zhenglai Li
Chang Tang
Suyuan Liu
Hao Yu
Chuan Tang
Miaomiao Li
Xinwang Liu

Most existing multi-view clustering methods aim to generate a consensus partition across all views, based on the assumption that all views share the same sample arrangement. However, in real-world scenarios, the collected data across different views is often unsynchronized, making it difficult to ensure consistent sample correspondence between views. To address this issue, we propose a scalable sample-alignment-based multi-view clustering method, referred to as SSA-MVC. Specifically, we first employ a cluster-label matching (CLM) algorithm to select the view whose clustering labels best match those of the others as the benchmark view. Then, for each of the remaining views, we construct representations of non-aligned samples by computing their similarities with aligned samples. Based on these representations, we build a similarity graph between the non-aligned samples of each view and those in the benchmark view, which serves as the alignment criterion. This alignment criterion is then integrated into a late-fusion framework to enable clustering without requiring aligned samples. Notably, the learned sample alignment matrix can be used to enhance existing multi-view clustering methods in scenarios where sample correspondence is unavailable. The effectiveness of the proposed SSA-MVC algorithm is validated through extensive experiments conducted on eight real-world multi-view datasets.

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NeurIPS Conference 2025 Conference Paper

SparseMVC: Probing Cross-view Sparsity Variations for Multi-view Clustering

Ruimeng Liu
Xin Zou
Chang Tang
Xiao Zheng
Xingchen Hu
Kun Sun
Xinwang Liu

Existing multi-view clustering methods employ various strategies to address data-level sparsity and view-level dynamic fusion. However, we identify a critical yet overlooked issue: varying sparsity across views. Cross-view sparsity variations lead to encoding discrepancies, heightening sample-level semantic heterogeneity and making view-level dynamic weighting inappropriate. To tackle these challenges, we propose Adaptive Sparse Autoencoders for Multi-View Clustering (SparseMVC), a framework with three key modules. Initially, the sparse autoencoder probes the sparsity of each view and adaptively adjusts encoding formats via an entropy-matching loss term, mitigating cross-view inconsistencies. Subsequently, the correlation-informed sample reweighting module employs attention mechanisms to assign weights by capturing correlations between early-fused global and view-specific features, reducing encoding discrepancies and balancing contributions. Furthermore, the cross-view distribution alignment module aligns feature distributions during the late fusion stage, accommodating datasets with an arbitrary number of views. Extensive experiments demonstrate that SparseMVC achieves state-of-the-art clustering performance. Our framework advances the field by extending sparsity handling from the data-level to view-level and mitigating the adverse effects of encoding discrepancies through sample-level dynamic weighting. The source code is publicly available at https: //github. com/cleste-pome/SparseMVC.

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IJCAI Conference 2025 Conference Paper

Spatially Resolved Transcriptomics Data Clustering with Tailored Spatial-scale Modulation

Yuang Xiao
Yanran Zhu
Chang Tang
Xiao Zheng
Yuanyuan Liu
Kun Sun
Xinwang Liu

Spatial transcriptomics, comprising spatial location and high-throughput gene expression information, provides revolutionary insights into disease discovery and cellular evolution. Spatial transcriptomic clustering, which pinpoints distinct spatial domains within tissues, reveals cellular interactions and enhances our understanding of the intricate architecture of tissues. Existing methods typically construct spatial graphs using a static radius based on spatial coordinates, which hinders the accurate identification of spatial domains and complicates the precise partitioning of boundary nodes within clusters. To address this issue, we introduce a novel spatially resolved transcriptomics data clustering network (TSstc). Specifically, we employ a tailored spatial-scale modulation approach, constructing different spatial graphs incrementally as the radius of the spatial domain expands, and a Spatiality-Aware Sampling (SAS) strategy is proposed to aggregate node representations by considering the spatial dependencies between spots. We then use GCN encoders to learn gene embedding with gene graph and multiple spatial embeddings with spatial graphs. During training, we incorporate cross-view correlation-based tailored spatial regularization constraints to preserve high-quality neighbor relationships across spatial embeddings at different scales. Finally, a zero-inflated negative binomial model is utilized to capture the global probability distribution of gene expression profiles. Extensive experimental results demonstrate that our approach surpasses existing state-of-the-art methods in clustering tasks and related downstream applications.

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AAAI Conference 2025 Conference Paper

Structure-Adaptive Multi-View Graph Clustering for Remote Sensing Data

Renxiang Guan
Wenxuan Tu
Siwei Wang
Jiyuan Liu
Dayu Hu
Chang Tang
Yu Feng
Junhong Li

Multi-view clustering (MVC) for remote sensing data is a critical and challenging task in Earth observation. Although recent advances in graph neural network (GNN)-based MVC have shown remarkable success, the most prevalent approaches have two major limitations: 1) heavily relying on a predefined yet fixed graph, which limits the performance of clustering because the large number of indistinguishable background samples contained in remote sensing data would introduce noise information and increase structure heterogeneity; 2) ignoring the effect of confusing samples on cluster structure compactness, which leads to fluffy cluster structure and decrease feature discriminability. To address these issues, we propose a Structure-Adaptive Multi-View Graph Clustering method named SAMVGC on remote sensing data which boosts the structure homogeneity and cluster compactness by adaptively learning the graph and cluster structures, respectively. Concretely, we use the geometric structure within the feature embedding space to refine adjacency matrices. The adjacency matrices are dynamically fused with the previous ones to improve the homogeneity and stability of structure information. Additionally, the samples are separated into two categories, including the central (intra-cluster center samples) and the confusing (inter-cluster boundary samples). On the basis, we deploy the contrastive learning paradigm on the central samples within views and the consistent learning paradigm on the confusing samples between views, improving the cluster compactness and consistency. Finally, we conduct extensive experiments on four benchmarks and achieve promising results, well demonstrating the effectiveness and superiority of the proposed method.

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JBHI Journal 2024 Journal Article

DAI-Net: Dual Adaptive Interaction Network for Coordinated Medication Recommendation

Xin Zou
Xiao He
Xiao Zheng
Wei Zhang
Jiajia Chen
Chang Tang

Medication recommendation is a productive task for AI-driven healthcare systems, which can assist clinicians in prescribing judicious and effective treatments. However, existing medication recommendation methods omit two key pieces of information: Coarse-grained interaction information between distinct types of symptoms in a patient's medical history and corresponding medication representations can serve as attention for predicting the current medication combinations of the patient. Fine-grained interaction information between medication substructure representations and different types of symptoms can facilitate the construction of molecular-level disentangled medication representations. To address this dilemma, we propose a novel D ual A daptive I nteraction Net work (DAI-Net), which encodes comprehensive interaction knowledge between patients' multifaceted health records and medication molecules to improve the performance of medication recommendation and heighten interpretability of the model. Specifically, we design a symptom-aware medication matching module to extract coordinated associations between patient symptoms and medication molecules, coarse-grained interaction learning. The medication embeddings are utilized to transform patient-medication matching properties into a symptom-substructure matching matrix for fine-grained interaction. The patient's Longitudinal representation is employed as a query to decode both symptom-medication and symptom-substructure matching information for coordinated medication representation. DAI-Net is an end-to-end recommendation model. Extensive experiments on the real-world EHR datasets, i. e. , the public benchmark MIMIC-III, MIMIC-IV, and eICU, demonstrate that the proposed DAI-Net achieves competitive performance compared to other state-of-the-art ones, with an average improvement of 1. 8%, 2. 1% in Jaccard on MIMIC-III and -IV dataset.

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JBHI Journal 2024 Journal Article

Hierarchical and Dynamic Graph Attention Network for Drug-Disease Association Prediction

Shuhan Huang
Minhui Wang
Xiao Zheng
Jiajia Chen
Chang Tang

In the realm of biomedicine, the prediction of associations between drugs and diseases holds significant importance. Yet, conventional wet lab experiments often fall short of meeting the stringent demands for prediction accuracy and efficiency. Many prior studies have predominantly focused on drug and disease similarities to predict drug-disease associations, but overlooking the crucial interactions between drugs and diseases that are essential for enhancing prediction accuracy. Hence, in this paper, a resilient and effective model named Hierarchical and Dynamic Graph Attention Network (HDGAT) has been proposed to predict drug-disease associations. Firstly, it establishes a heterogeneous graph by leveraging the interplay of drug and disease similarities and associations. Subsequently, it harnesses the capabilities of graph convolutional networks and bidirectional long short-term memory networks (Bi-LSTM) to aggregate node-level information within the heterogeneous graph comprehensively. Furthermore, it incorporates a hierarchical attention mechanism between convolutional layers and a dynamic attention mechanism between nodes to learn embeddings for drugs and diseases. The hierarchical attention mechanism assigns varying weights to embeddings learned from different convolutional layers, and the dynamic attention mechanism efficiently prioritizes inter-node information by allocating each node with varying rankings of attention coefficients for neighbour nodes. Moreover, it employs residual connections to alleviate the over-smoothing issue in graph convolution operations. The latent drug-disease associations are quantified through the fusion of these embeddings ultimately. By conducting 5-fold cross-validation, HDGAT's performance surpasses the performance of existing state-of-the-art models across various evaluation metrics, which substantiates the exceptional efficacy of HDGAT in predicting drug-disease associations.

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EAAI Journal 2024 Journal Article

Hyperspectral band selection via region-wise latent feature fusion and graph filter embedded subspace clustering

Wei Feng
Minhui Wang
Chang Tang
Weiying Xie
Xianju Li
Xiao Zheng
Jiangfeng Xu

Hyperspectral band selection plays a crucial role in reducing dimensionality, extracting relevant features, and improving computational efficiency in hyperspectral data analysis. Although numerous band selection methods have emerged in recent years, there remains a significant gap in exploring spatial structures and the diversity of ground objects. In this paper, we propose a region-wise latent feature fusion and graph filter embedded subspace clustering approach to address the band selection problem. Specifically, we segment the original hyperspectral image into diverse homogeneous regions using entropy rate superpixel segmentation. Next, we fuse the features from these regions into a consensus low-dimensional latent space, effectively capturing spatial information. To explore the spectral correlation among all bands, we employ a self-representation subspace clustering model on the fused latent features. Additionally, we apply a graph filter to the raw region-wise features to reduce redundant and noisy information present in the original data. By integrating these concerns into a unified framework, we facilitate mutual reinforcement among the learning sub-tasks. We conduct experiments on four public hyperspectral datasets, demonstrating a 1. 93%, 0. 50%, 0. 76%, and 0. 94% accuracy enhancement over current state-of-the-art methods. Additionally, our proposed optimization method enables the model to converge rapidly in under 15 iterations.

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AAAI Conference 2024 Conference Paper

Sample-Level Cross-View Similarity Learning for Incomplete Multi-View Clustering

Suyuan Liu
Junpu Zhang
Yi Wen
Xihong Yang
Siwei Wang
Yi Zhang
En Zhu
Chang Tang

Incomplete multi-view clustering has attracted much attention due to its ability to handle partial multi-view data. Recently, similarity-based methods have been developed to explore the complete relationship among incomplete multi-view data. Although widely applied to partial scenarios, most of the existing approaches are still faced with two limitations. Firstly, fusing similarities constructed individually on each view fails to yield a complete unified similarity. Moreover, incomplete similarity generation may lead to anomalous similarity values with column sum constraints, affecting the final clustering results. To solve the above challenging issues, we propose a Sample-level Cross-view Similarity Learning (SCSL) method for Incomplete Multi-view Clustering. Specifically, we project all samples to the same dimension and simultaneously construct a complete similarity matrix across views based on the inter-view sample relationship and the intra-view sample relationship. In addition, a simultaneously learning consensus representation ensures the validity of the projection, which further enhances the quality of the similarity matrix through the graph Laplacian regularization. Experimental results on six benchmark datasets demonstrate the ability of SCSL in processing incomplete multi-view clustering tasks. Our code is publicly available at https://github.com/Tracesource/SCSL.

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EAAI Journal 2024 Journal Article

Token-disentangling Mutual Transformer for multimodal emotion recognition

Guanghao Yin
Yuanyuan Liu
Tengfei Liu
Haoyu Zhang
Fang Fang
Chang Tang
Liangxiao Jiang

Multimodal emotion recognition presents a complex challenge, as it involves the identification of human emotions using various modalities such as video, text, and audio. Existing methods focus mainly on the fusion information from multimodal data, but ignore the interaction of the modality-specific heterogeneity features that contribute differently to emotions, leading to sub-optimal results. To tackle this challenge, we propose a novel Token-disentangling Mutual Transformer (TMT) for robust multimodal emotion recognition, by effectively disentangling and interacting inter-modality emotion consistency features and intra-modality emotion heterogeneity features. Specifically, the TMT consists of two main modules: multimodal emotion Token disentanglement and Token mutual Transformer. In the multimodal emotion Token disentanglement, we introduce a Token separation encoder with an elaborated Token disentanglement regularization, which effectively disentangle the inter-modality emotion consistency feature Token from each intra-modality emotion heterogeneity feature Token; consequently, the emotion-related consistency and heterogeneity information can be performed independently and comprehensively. Furthermore, we devise the Token mutual Transformer with two cross-modal encoders to interact and fuse the disentangled feature Tokens by using bi-directional query learning, which delivers more comprehensive and complementary multimodal emotion representations for multimodal emotion recognition. We evaluate our model on three popular three-modality emotion datasets, namely CMU-MOSI, CMU-MOSEI, and CH-SIMS, and the experimental results affirm the superior performance of our model compared to state-of-the-art methods, achieving state-of-the-art recognition performance. Evaluation Codes and models are released at https: //github. com/cug-ygh/TMT.

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AAAI Conference 2023 Conference Paper

Multi-Level Confidence Learning for Trustworthy Multimodal Classification

Xiao Zheng
Chang Tang
Zhiguo Wan
Chengyu Hu
Wei Zhang

With the rapid development of various data acquisition technologies, more and more multimodal data come into being. It is important to integrate different modalities which are with high-dimensional features for boosting final multimodal data classification task. However, existing multimodal classification methods mainly focus on exploiting the complementary information of different modalities, while ignoring the learning confidence during information fusion. In this paper, we propose a trustworthy multimodal classification network via multi-level confidence learning, referred to as MLCLNet. Considering that a large number of feature dimensions could not contribute to final classification performance but disturb the discriminability of different samples, we propose a feature confidence learning mechanism to suppress some redundant features, as well as enhancing the expression of discriminative feature dimensions in each modality. In order to capture the inherent sample structure information implied in each modality, we design a graph convolutional network branch to learn the corresponding structure preserved feature representation and generate modal-specific initial classification labels. Since samples from different modalities should share consistent labels, a cross-modal label fusion module is deployed to capture the label correlations of different modalities. In addition, motivated the ideally orthogonality of final fused label matrix, we design a label confidence loss to supervise the network for learning more separable data representations. To the best of our knowledge, MLCLNet is the first work which integrates both feature and label-level confidence learning for multimodal classification. Extensive experiments on four multimodal medical datasets are conducted to validate superior performance of MLCLNet when compared to other state-of-the-art methods.

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IJCAI Conference 2021 Conference Paper

Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

Chang Tang
Xinwang Liu
En Zhu
Lizhe Wang
Albert Zomaya

In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. Considering that different objects have different reflection characteristics, we use a superpixel segmentation algorithm to segment the first principal component of original hyperspectral image cube into homogeneous regions. For each superpixel, we construct a corresponding similarity graph to reflect the similarity between band pairs. Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering. During the graph diffusion process, the spatial and spectral information of each superpixel are embedded to make spatial/spectral similar superpixels contribute more to each other. Finally, the band containing minimum noise in each subcube is selected to represent the whole subcube. Extensive experiments are conducted on three public datasets to validate the superiority of the proposed method when compared with other state-of-the-art ones.

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ICML Conference 2021 Conference Paper

One Pass Late Fusion Multi-view Clustering

Xinwang Liu 0002
Li Liu 0002
Qing Liao 0001
Siwei Wang 0001
Yi Zhang 0104
Wenxuan Tu
Chang Tang
Jiyuan Liu 0003

Existing late fusion multi-view clustering (LFMVC) optimally integrates a group of pre-specified base partition matrices to learn a consensus one. It is then taken as the input of the widely used k-means to generate the cluster labels. As observed, the learning of the consensus partition matrix and the generation of cluster labels are separately done. These two procedures lack necessary negotiation and can not best serve for each other, which may adversely affect the clustering performance. To address this issue, we propose to unify the aforementioned two learning procedures into a single optimization, in which the consensus partition matrix can better serve for the generation of cluster labels, and the latter is able to guide the learning of the former. To optimize the resultant optimization problem, we develop a four-step alternate algorithm with proved convergence. We theoretically analyze the clustering generalization error of the proposed algorithm on unseen data. Comprehensive experiments on multiple benchmark datasets demonstrate the superiority of our algorithm in terms of both clustering accuracy and computational efficiency. It is expected that the simplicity and effectiveness of our algorithm will make it a good option to be considered for practical multi-view clustering applications.

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AAAI Conference 2020 Conference Paper

CGD: Multi-View Clustering via Cross-View Graph Diffusion

Chang Tang
Xinwang Liu
Xinzhong Zhu
En Zhu
Zhigang Luo
Lizhe Wang
Wen Gao

Graph based multi-view clustering has been paid great attention by exploring the neighborhood relationship among data points from multiple views. Though achieving great success in various applications, we observe that most of previous methods learn a consensus graph by building certain data representation models, which at least bears the following drawbacks. First, their clustering performance highly depends on the data representation capability of the model. Second, solving these resultant optimization models usually results in high computational complexity. Third, there are often some hyperparameters in these models need to tune for obtaining the optimal results. In this work, we propose a general, effective and parameter-free method with convergence guarantee to learn a uniﬁed graph for multi-view data clustering via cross-view graph diffusion (CGD), which is the ﬁrst attempt to employ diffusion process for multi-view clustering. The proposed CGD takes the traditional predeﬁned graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by 1) capturing the underlying manifold geometry structure of original data points, and 2) leveraging the complementary information among multiple graphs. The ﬁnal uniﬁed graph used for clustering is obtained by averaging the improved view associated graphs. Extensive experiments on several benchmark datasets are conducted to demonstrate the effectiveness of the proposed method in terms of seven clustering evaluation metrics.

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AAAI Conference 2020 Conference Paper

R²MRF: Defocus Blur Detection via Recurrently Refining Multi-Scale Residual Features

Chang Tang
Xinwang Liu
Xinzhong Zhu
En Zhu
Kun Sun
Pichao Wang
Lizhe Wang
Albert Zomaya

Defocus blur detection aims to separate the in-focus and out-of-focus regions in an image. Although attracting more and more attention due to its remarkable potential applications, there are still several challenges for accurate defocus blur detection, such as the interference of background clutter, sensitivity to scales and missing boundary details of defocus blur regions. In order to address these issues, we propose a deep neural network which Recurrently Refines Multi-scale Residual Features (R2MRF) for defocus blur detection. We firstly extract multi-scale deep features by utilizing a fully convolutional network. For each layer, we design a novel recurrent residual refinement branch embedded with multiple residual refinement modules (RRMs) to more accurately detect blur regions from the input image. Considering that the features from bottom layers are able to capture rich low-level features for details preservation while the features from top layers are capable of characterizing the semantic information for locating blur regions, we aggregate the deep features from different layers to learn the residual between the intermediate prediction and the ground truth for each recurrent step in each residual refinement branch. Since the defocus degree is sensitive to image scales, we finally fuse the side output of each branch to obtain the final blur detection map. We evaluate the proposed network on two commonly used defocus blur detection benchmark datasets by comparing it with other 11 state-of-the-art methods. Extensive experimental results with ablation studies demonstrate that R2MRF consistently and significantly outperforms the competitors in terms of both efficiency and accuracy.

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AAAI Conference 2019 Conference Paper

Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection

Chang Tang
Xinzhong Zhu
Xinwang Liu
Lizhe Wang

Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from multi-view data without using the labels of samples. However, we observe that existing MV-UFS algorithms do not well consider the local structure of cross views and the diversity of different views, which could adversely affect the performance of subsequent learning tasks. In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues. Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views. Further, an inter-view similarity graph between each pairwise view and an intra-view similarity graph of each view are respectively constructed to preserve the local structure of data in different views and different samples in the same view. An l2, 1-norm constraint is imposed on the feature projection matrix to select discriminative features. We carefully design an efficient algorithm with convergence guarantee to solve the resultant optimization problem. Extensive experimental study is conducted on six publicly real multi-view datasets and the experimental results well demonstrate the effectiveness of CRV-DCL.

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AAAI Conference 2019 Conference Paper

Efficient and Effective Incomplete Multi-View Clustering

Xinwang Liu
Xinzhong Zhu
Miaomiao Li
Chang Tang
En Zhu
Jianping Yin
Wen Gao

Incomplete multi-view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, overcomplicated optimization and limitedly improved clustering performance. In this paper, we propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues. Instead of completing the incomplete kernel matrices, EE-IMVC proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix. We carefully develop a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Further, we conduct comprehensive experiments to study the proposed EE-IMVC in terms of clustering accuracy, running time, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time and memory.

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IJCAI Conference 2019 Conference Paper

Multi-view Clustering via Late Fusion Alignment Maximization

Siwei Wang
Xinwang Liu
En Zhu
Chang Tang
Jiyuan Liu
Jingtao Hu
Jingyuan Xia
Jianping Yin

Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in many applications, we observe that most of existing methods directly combine multiple views to learn an optimal similarity for clustering. These methods would cause intensive computational complexity and over-complicated optimization. In this paper, we theoretically uncover the connection between existing k-means clustering and the alignment between base partitions and consensus partition. Based on this observation, we propose a simple but effective multi-view algorithm termed {Multi-view Clustering via Late Fusion Alignment Maximization (MVC-LFA)}. In specific, MVC-LFA proposes to maximally align the consensus partition with the weighted base partitions. Such a criterion is beneficial to significantly reduce the computational complexity and simplify the optimization procedure. Furthermore, we design a three-step iterative algorithm to solve the new resultant optimization problem with theoretically guaranteed convergence. Extensive experiments on five multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed MVC-LFA.

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