Jun Wan

EAAI Journal 2026 Journal Article

Leveraging community context and frequency-adaptive aggregation for robust fraud detection

• Zheng Zhang
• Jun Wan
• Jun Liu
• Mingyang Zhou
• Kezhong Lu
• Claudio J. Tessone
• Guoliang Chen
• Hao Liao

AAAI Conference 2026 Conference Paper

SpaCRD: Multimodal Deep Fusion of Histology and Spatial Transcriptomics for Cancer Region Detection

• Shuailin Xue
• Jun Wan
• Lihua Zhang
• Wenwen Min

Accurate detection of cancer tissue regions (CTR) enables deeper analysis of the tumor microenvironment and offers crucial insights into treatment response. Traditional CTR detection methods, which typically rely on the rich cellular morphology in histology images, are susceptible to a high rate of false positives due to morphological similarities across different tissue regions. The groundbreaking advances in spatial transcriptomics (ST) provide detailed cellular phenotypes and spatial localization information, offering new opportunities for more accurate cancer region detection. However, current methods are unable to effectively integrate histology images with ST data, especially in the context of cross-sample and cross-platform/batch settings for accomplishing the CTR detection. To address this challenge, we propose SpaCRD, a transfer learning-based method that deeply integrates histology images and ST data to enable reliable CTR detection across diverse samples, platforms, and batches. Once trained on source data, SpaCRD can be readily generalized to accurately detect cancerous regions across samples from different platforms and batches. The core of SpaCRD is a category-regularized variational reconstruction-guided bidirectional cross-attention fusion network, which enables the model to adaptively capture latent co-expression patterns between histological features and gene expression from multiple perspectives. Extensive benchmark analysis on 23 matched histology-ST datasets spanning various disease types, platforms, and batches demonstrates that SpaCRD consistently outperforms existing eight state-of-the-art methods in CTR detection.

EAAI Journal 2025 Journal Article

Adaptive feature selection with flexible mapping for diagnosis and prediction of Parkinson's disease

• Zhongwei Huang
• Jianqiang Li
• Jiatao Yang
• Jun Wan
• Jianxia Chen
• Zhi Yang
• Ming Shi
• Ran Zhou

JBHI Journal 2025 Journal Article

Incomplete Multi-view Data Learning via Adaptive Embedding and Partial l 2,1 Norm Constraints for Parkinson's Disease Diagnosis

• Zhongwei Huang
• Kai Wang
• Chao Chen
• Jianxia Chen
• Jun Wan
• Zhi Yang
• Ran Zhou
• Haitao Gan

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by mental abnormalities and motor dysfunction. Its early classification and prediction of clinical scores have been major concerns for researchers. Currently, multi-view data learning has become an essential research area due to the capacity of multiple views to provide complementary insights from various perspectives. However, the discontinuous distribution, data missing complexity, small sample size, and redundant features in multi-view datasets pose a substantial obstacle, and most existing multi-view learning methods are unable to handle these challenges effectively. In this study, we propose a novel incomplete multi-view data learning framework (IMVDL) via dynamic embedding and partial l 2, 1 norm constraints for PD diagnosis. Specifically, multi-view dynamic embedding can adapt to any view missing scene, thereby linearly/nonlinearly mapping incomplete multi-view data to low-dimensional manifold spaces and generating complete multi-view data representations. The partial l 2, 1 norm constraint can ignore larger feature weight values and perform l 2, 1 norm sparse on the remaining weights, thereby avoiding the sparse bias problem caused by larger weight values. An efficient iterative algorithm is derived to find the optimal solution of the IMVDL method. We conduct extensive experiments using multi-modal neuroimage data from the Parkinson's Progression Markers Initiative (PPMI) database. The results demonstrate that the IMVDL method is superior to other comparative methods. The source code for IMVDL is available at https://github.com/a610lab/IMVDL/.

AAAI Conference 2025 Conference Paper

Mixture-of-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detection

• Shunxin Chen
• Ajian Liu
• Junze Zheng
• Jun Wan
• Kailai Peng
• Sergio Escalera
• Zhen Lei

Unified detection of digital and physical attacks in facial recognition systems has become a focal point of research in recent years. However, current multi-modal methods typically ignore the intra-class and inter-class variability across different types of attacks, leading to degraded performance. To address this limitation, we propose MoAE-CR, a framework that effectively leverages class-aware information for improved attack detection. Our improvements manifest at two levels, i.e., the feature and loss level. At the feature level, we propose Mixture-of-Attack-Experts (MoAEs) to capture more subtle differences among various types of fake faces. At the loss level, we introduce Class Regularization (CR) through the Disentanglement Module (DM) and the Cluster Distillation Module (CDM). The DM enhances class separability by increasing the distance between the centers of live and fake face classes. However, center-to-center constraints alone are insufficient to ensure distinctive representations for individual features. Thus, we propose the CDM to further cluster features around their class centers while maintaining separation from other classes. Moreover, specific attacks that significantly deviate from common attack patterns are often overlooked. To address this issue, our distance calculation prioritizes more distant features. Extensive experiments on two unified physical-digital attack datasets demonstrate the state-of-the-art performance of the proposed method.

AAAI Conference 2024 Conference Paper

Compound Text-Guided Prompt Tuning via Image-Adaptive Cues

• Hao Tan
• Jun Li
• Yizhuang Zhou
• Jun Wan
• Zhen Lei
• Xiangyu Zhang

Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable generalization capabilities to downstream tasks. However, existing prompt tuning based frameworks need to parallelize learnable textual inputs for all categories, suffering from massive GPU memory consumption when there is a large number of categories in the target dataset. Moreover, previous works require to include category names within prompts, exhibiting subpar performance when dealing with ambiguous category names. To address these shortcomings, we propose Compound Text-Guided Prompt Tuning (TGP-T) that significantly reduces resource demand while achieving superior performance. We introduce text supervision to the optimization of prompts, which enables two benefits: 1) releasing the model reliance on the pre-defined category names during inference, thereby enabling more flexible prompt generation; 2) reducing the number of inputs to the text encoder, which decreases GPU memory consumption significantly. Specifically, we found that compound text supervisions, i.e., category-wise and content-wise, is highly effective, since they provide inter-class separability and capture intra-class variations, respectively. Moreover, we condition the prompt generation on visual features through a module called Bonder, which facilitates the alignment between prompts and visual features. Extensive experiments on few-shot recognition and domain generalization demonstrate that TGP-T achieves superior performance with consistently lower training costs. It reduces GPU memory usage by 93% and attains a 2.5% performance gain on 16-shot ImageNet. The code is available at https://github.com/EricTan7/TGP-T.

IJCAI Conference 2024 Conference Paper

Unified Physical-Digital Face Attack Detection

• Hao Fang
• Ajian Liu
• Haocheng Yuan
• Junze Zheng
• Dingheng Zeng
• Yanhong Liu
• Jiankang Deng
• Sergio Escalera

Face Recognition (FR) systems can suffer from physical (i. e. , print photo) and digital (i. e. , DeepFake) attacks. However, previous related work rarely considers both situations at the same time. This implies the deployment of multiple models and thus more computational burden. The main reasons for this lack of an integrated model are caused by two factors: (1) The lack of a dataset including both physical and digital attacks which the same ID covers the real face and all attack types; (2) Given the large intra-class variance between these two attacks, it is difficult to learn a compact feature space to detect both attacks simultaneously. To address these issues, we collect a Unified physical-digital Attack dataset, called UniAttackData. The dataset consists of 1, 800 participations of 2 and 12 physical and digital attacks, respectively, resulting in a total of 28, 706 videos. Then, we propose a Unified Attack Detection framework based on Vision-Language Models (VLMs), namely UniAttackDetection, which includes three main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring unified and specific knowledge respectively; the Unified Knowledge Mining (UKM) module, designed to capture a comprehensive feature space; and the Sample-Level Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics. These three modules seamlessly form a robust unified attack detection framework. Extensive experiments on UniAttackData and three other datasets demonstrate the superiority of our approach for unified face attack detection. Dataset link: https: //sites. google. com/view/face-anti-spoofing-challenge/dataset-download/uniattackdatacvpr2024

IJCAI Conference 2023 Conference Paper

Cross-Domain Facial Expression Recognition via Disentangling Identity Representation

• Tong Liu
• Jing Li
• Jia Wu
• Lefei Zhang
• Shanshan Zhao
• Jun Chang
• Jun Wan

Most existing cross-domain facial expression recognition (FER) works require target domain data to assist the model in analyzing distribution shifts to overcome negative effects. However, it is often hard to obtain expression images of the target domain in practical applications. Moreover, existing methods suffer from the interference of identity information, thus limiting the discriminative ability of the expression features. We exploit the idea of domain generalization (DG) and propose a representation disentanglement model to address the above problems. Specifically, we learn three independent potential subspaces corresponding to the domain, expression, and identity information from facial images. Meanwhile, the extracted expression and identity features are recovered as Fourier phase information reconstructed images, thereby ensuring that the high-level semantics of images remain unchanged after disentangling the domain information. Our proposed method can disentangle expression features from expression-irrelevant ones (i. e. , identity and domain features). Therefore, the learned expression features exhibit sufficient domain invariance and discriminative ability. We conduct experiments with different settings on multiple benchmark datasets, and the results show that our method achieves superior performance compared with state-of-the-art methods.

AAAI Conference 2023 Conference Paper

Mixture Uniform Distribution Modeling and Asymmetric Mix Distillation for Class Incremental Learning

• Sunyuan Qiang
• Jiayi Hou
• Jun Wan
• Yanyan Liang
• Zhen Lei
• Du Zhang

Exemplar rehearsal-based methods with knowledge distillation (KD) have been widely used in class incremental learning (CIL) scenarios. However, they still suffer from performance degradation because of severely distribution discrepancy between training and test set caused by the limited storage memory on previous classes. In this paper, we mathematically model the data distribution and the discrepancy at the incremental stages with mixture uniform distribution (MUD). Then, we propose the asymmetric mix distillation method to uniformly minimize the error of each class from distribution discrepancy perspective. Specifically, we firstly promote mixup in CIL scenarios with the incremental mix samplers and incremental mix factor to calibrate the raw training data distribution. Next, mix distillation label augmentation is incorporated into the data distribution to inherit the knowledge information from the previous models. Based on the above augmented data distribution, our trained model effectively alleviates the performance degradation and extensive experimental results validate that our method exhibits superior performance on CIL benchmarks.

AAAI Conference 2021 Conference Paper

Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition

• Benjia Zhou
• Yunan Li
• Jun Wan

Human gesture recognition has drawn much attention in the area of computer vision. However, the performance of gesture recognition is always influenced by some gesture-irrelevant factors like the background and the clothes of performers. Therefore, focusing on the regions of hand/arm is important to the gesture recognition. Meanwhile, a more adaptive architecture-searched network structure can also perform better than the block-fixed ones like ResNet since it increases the diversity of features in different stages of the network better. In this paper, we propose a regional attention with architecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replace the fixed Inception modules with the automatically rebuilt structure through the network via Neural Architecture Search (NAS), owing to the different shape and representation ability of features in the early, middle, and late stage of the network. It enables the network to capture different levels of feature representations at different layers more adaptively. Meanwhile, we also design a stackable regional attention module called Dynamic- Static Attention (DSA), which derives a Gaussian guidance heatmap and dynamic motion map to highlight the hand/arm regions and the motion information in the spatial and temporal domains, respectively. Extensive experiments on two recent large-scale RGB-D gesture datasets validate the effectiveness of the proposed method and show it outperforms state-of-the-art methods. The codes of our method are available at: https: //github. com/zhoubenjia/RAAR3DNet.

AAAI Conference 2020 Conference Paper

Relation-Aware Pedestrian Attribute Recognition with Graph Convolutional Networks

• Zichang Tan
• Yang Yang
• Jun Wan
• Guodong Guo
• Stan Z. Li

In this paper, we propose a new end-to-end network, named Joint Learning of Attribute and Contextual relations (JLAC), to solve the task of pedestrian attribute recognition. It includes two novel modules: Attribute Relation Module (ARM) and Contextual Relation Module (CRM). For ARM, we construct an attribute graph with attribute-specific features which are learned by the constrained losses, and further use Graph Convolutional Network (GCN) to explore the correlations among multiple attributes. For CRM, we first propose a graph projection scheme to project the 2-D feature map into a set of nodes from different image regions, and then employ GCN to explore the contextual relations among those regions. Since the relation information in the above two modules is correlated and complementary, we incorporate them into a unified framework to learn both together. Experiments on three benchmarks, including PA-100K, RAP, PETA attribute datasets, demonstrate the effectiveness of the proposed JLAC.

IJCAI Conference 2019 Conference Paper

Deeply-learned Hybrid Representations for Facial Age Estimation

• Zichang Tan
• Yang Yang
• Jun Wan
• Guodong Guo
• Stan Z. Li

In this paper, we propose a novel unified network named Deep Hybrid-Aligned Architecture for facial age estimation. It contains global, local and global-local branches. They are jointly optimized and thus can capture multiple types of features with complementary information. In each branch, we employ a separate loss for each sub-network to extract the independent features and use a recurrent fusion to explore correlations among those region features. Considering that the pose variations may lead to misalignment in different regions, we design an Aligned Region Pooling operation to generate aligned region features. Moreover, a new large age dataset named Web-FaceAge owning more than 120K samples is collected under diverse scenes and spanning a large age range. Experiments on five age benchmark datasets, including Web-FaceAge, Morph, FG-NET, CACD and Chalearn LAP 2015, show that the proposed method outperforms the state-of-the-art approaches significantly.

AAAI Conference 2018 Conference Paper

Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition

• Pichao Wang
• Wanqing Li
• Jun Wan
• Philip Ogunbona
• Xinwang Liu

A novel deep neural network training paradigm that exploits the conjoint information in multiple heterogeneous sources is proposed. Specifically, in a RGB-D based action recognition task, it cooperatively trains a single convolutional neural network (named c-ConvNet) on both RGB visual features and depth features, and deeply aggregates the two kinds of features for action recognition. Differently from the conventional ConvNet that learns the deep separable features for homogeneous modality-based classification with only one softmax loss function, the c-ConvNet enhances the discriminative power of the deeply learned features and weakens the undesired modality discrepancy by jointly optimizing a ranking loss and a softmax loss for both homogeneous and heterogeneous modalities. The ranking loss consists of intra-modality and cross-modality triplet losses, and it reduces both the intra-modality and crossmodality feature variations. Furthermore, the correlations between RGB and depth data are embedded in the c-ConvNet, and can be retrieved by either of the modalities and contribute to the recognition in the case even only one of the modalities is available. The proposed method was extensively evaluated on two large RGB-D action recognition datasets, ChaLearn LAP IsoGD and NTU RGB+D datasets, and one small dataset, SYSU 3D HOI, and achieved state-of-the-art results.

JMLR Journal 2013 Journal Article

One-shot Learning Gesture Recognition from RGB-D Data Using Bag of Features

• Jun Wan
• Qiuqi Ruan
• Wei Li
• Shuang Deng

For one-shot learning gesture recognition, two important challenges are: how to extract distinctive features and how to learn a discriminative model from only one training sample per gesture class. For feature extraction, a new spatio-temporal feature representation called 3D enhanced motion scale-invariant feature transform (3D EMoSIFT) is proposed, which fuses RGB-D data. Compared with other features, the new feature set is invariant to scale and rotation, and has more compact and richer visual representations. For learning a discriminative model, all features extracted from training samples are clustered with the k-means algorithm to learn a visual codebook. Then, unlike the traditional bag of feature (BoF) models using vector quantization (VQ) to map each feature into a certain visual codeword, a sparse coding method named simulation orthogonal matching pursuit (SOMP) is applied and thus each feature can be represented by some linear combination of a small number of codewords. Compared with VQ, SOMP leads to a much lower reconstruction error and achieves better performance. The proposed approach has been evaluated on ChaLearn gesture database and the result has been ranked amongst the top best performing techniques on ChaLearn gesture challenge (round 2). [abs] [ pdf ][ bib ] &copy JMLR 2013. ( edit, beta )