Wen Jie

• Doctor of Engineering
• Associate Proffersor at Harbin Institute of Technology Shenzhen

Multi-view clustering (MVC) aims to exploit the latent relationships between heterogeneous samples in an unsupervised manner, which has served as a fundamental task in the unsupervised learning community and has drawn widespread attention. In this work, we propose a new deep multi-view contrastive clustering method via graph structure awareness (DM...

Video anomaly detection (VAD) aims at locating the abnormal events in videos. Recently, the Weakly Supervised VAD has made great progress, which only requires video-level annotations when training. In practical applications, different institutions may have different types of abnormal videos. However, the abnormal videos cannot be circulated on the...

The objective of referring expression comprehension (REC) is to accurately identify the object in an image described by a given expression. Existing REC methods, including transformer-based and graph-based approaches among others, have shown robust performance in REC tasks. In this study, we present a groundbreaking framework named DiffusionREC for...

With the advancement of computer vision, numerous models have been proposed for screening of fundus diseases. However, the recognition of multiple fundus diseases is often hampered by the simultaneous presence of multiple disease types and the confluence of lesion types in fundus images. This paper addresses these challenges by conceptualizing them...

Deep online cross-modal hashing has gained much attention from researchers recently, as its promising applications with low storage requirement, fast retrieval efficiency and cross modality adaptive, etc. However, there still exists some technical hurdles that hinder its applications, e.g., 1) how to extract the coexistent semantic relevance of cro...

Medical image segmentation provides useful information about the shape and size of organs, which is beneficial for improving diagnosis, analysis, and treatment. Despite traditional deep learning-based models can extract domain-specific knowledge, they face a generalization bottleneck due to the limited embedded knowledge scope. Vision foundation mo...

Edit-based approaches for Grammatical Error Correction (GEC) have attracted volume attention due to their outstanding explanations of the correction process and rapid inference. Through exploring the characteristics of the generalized and specific knowledge learning for GEC, we discover that efficiently training GEC systems with satisfactory genera...

Deep online cross-modal hashing has gained much attention from researchers recently, as its promising applications with low storage requirement, fast retrieval efficiency and cross modality adaptive, etc. However, there still exists some technical hurdles that hinder its applications, e.g., 1) how to extract the coexistent semantic relevance of cro...

Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance. However, most of the existing clustering methods often neglect to fully mine multi-view structural information and fail to explore the distribution of multi-view data, limiting clustering performance. To address these limitati...

Cross-modal hash learning has drawn widespread attention for large-scale multimodal retrieval because of its stability and efficiency in approximate similarity searches. However, most existing cross-modal hashing approaches employ discrete label-guided information to coarsely reflect intra- and intermodality correlations, making them less effective...

In the field of multi-view multi-label learning, the challenges of incomplete views and missing labels are prevalent due to the complexity of manual labeling and data acquisition errors. These challenges significantly reduce the quality of latent representations and hinder prediction by multi-label classification. To address this issue, we propose...

Cross-modal retrieval is a promising technique nowadays to find semantically similar instances in other modalities while a query instance is given from one modality. However, there still exists many challenges for reducing heterogeneous modality gap by embedding label information to discrete hash codes effectively, solving the binary optimization w...

Exploring the structure information is crucial for data clustering task, particularly for the sceneries of incomplete multiview clustering (IMVC) when some views are missing. However, almost all of the existing graph-based IMVC methods either introduce the Laplacian constraint with fixed graphs or simply fuse the graphs of all views, which are vuln...

Recently, the topic of multi-view multi-label classification has aroused significant attention from scholars. Plenty of methods adopt an average weighting scheme to merge the features obtained from multiple views, which commonly ignore the quality difference of information provided by multiple views and thus limit the credibility of the fusion feat...

In few-shot image classification tasks, methods based on pretrained vision-language models (such as CLIP) have achieved significant progress. Many existing approaches directly utilize visual or textual features as class prototypes, however, these features fail to adequately represent their respective classes. We identify that this limitation arises...

In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplete training data due to limitations in data collection and unreliable annotation processes. The absence of multi-view features impairs the comprehensive understanding of samples, omitting crucial details essential for classification. To address this i...

Due to the inefficiency of pixel-level annotations, weakly supervised salient object detection with image-category labels (WSSOD) has been receiving increasing attention. Previous works usually endeavor to generate high-quality pseudolabels to train the detectors in a fully supervised manner. However, we find that the detection performance is often...

Consensus representation learning is one of the most popular approaches in the field of multi-view clustering. However, most of the existing methods cannot learn discriminative representations with a clustering-friendly structure since these methods ignore the separation among clusters and the compactness within each cluster. To tackle this issue,...

Multi-view representation learning, aimed at uncovering the inherent structure within multi-view data, has developed rapidly in recent years. In practice, due to temporal and spatial desynchronization, it is common that only part of the data is aligned between views, which leads to the Partial View Alignment (PVA) problem. To address the challeng...

Video anomaly detection (VAD) is the core problem of intelligent video surveillance. Previous methods commonly adopt the unsupervised paradigm of frame reconstruction or prediction. However, the lack of mining of temporal dependent relationships and diversified event patterns within videos limit the performance of existing methods. To tackle these...

Unconstrained palmprint images have shown great potential for recognition applications due to their lower restrictions regarding hand poses and backgrounds during contactless image acquisition. However, they face two challenges: 1) Unclear palm contours and finger-valley points of unconstrained palmprint images make it difficult to locate landmarks...

Since hand-print recognition, i.e., palmprint, finger-knuckle-print (FKP), and hand-vein, have significant superiority in user convenience and hygiene, it has attracted greater enthusiasm from researchers. Seeking to handle the long-standing interference factors, i.e., noise, rotation, shadow, in hand-print images, multi-view hand-print representat...

Palmprint has attracted increasing attention for biometric recognition in recent years due to its outstanding reliability, user-friendliness and hygiene. However, existing palmprint recognition methods usually require high-quality palmprint images with clear texture and line patterns; however, in practical applications palmprint images are usually...

Incomplete multi-view clustering (IMVC) aims to reveal shared clustering structures within multi-view data, where only partial views of the samples are available. Existing IMVC methods primarily suffer from two issues: 1) Imputation-based methods inevitably introduce inaccurate imputations, which in turn degrade clustering performance; 2) Imputatio...

As a combination of emerging multi-view learning methods and traditional multi-label classification tasks, multi-view multi-label classification has shown broad application prospects. The diverse semantic information contained in heterogeneous data effectively enables the further development of multi-label classification. However, the widespread in...

Recently, multi-view multi-label classification (MvMLC) has received a significant amount of research interest and many methods have been proposed based on the assumptions of view completion and label completion. However, in real-world scenarios, multi-view multi-label data tends to be incomplete due to various uncertainties involved in data collec...

Diabetic Retinopathy (DR), the leading cause of blindness in diabetic patients, is diagnosed by the condition of retinal multiple lesions. As a difficult task in medical image segmentation, DR multi-lesion segmentation faces the main concerns as follows. On the one hand, retinal lesions vary in location, shape, and size. On the other hand, because...

Incomplete multiview clustering (IMVC) has received extensive attention in recent years. However, existing works still have several shortcomings: 1) some works ignore the correlation of sample pairs in the global structural distribution; 2) many methods are computational expensive, thus cannot be applicable to the large-scale incomplete data cluste...

Palmprint recognition has shown great value for biometric recognition due to its advantages of good hygiene, semi-privacy and low invasiveness. However, most existing palmprint recognition studies focus only on homogeneous palmprint recognition, where comparing palmprint images are collected under similar conditions with small domain gaps. To addre...

Three significant challenges have been limiting the stable palmprint recognition via mobile devices: 1) rotations and unconsensus scales of the unconstrait hand; 2) noises generated in the open imaging environments; and 3) low quality images captured in the low-illumination conditions. Current palmprint representation methods rely on rich prior kno...

Most current multi-view clustering methods necessitate that a sample's features be view-aligned or at least partially aligned across different views. Regrettably, real-world applications often fail to meet this requirement due to spatial, temporal, or spatiotemporal mismatches, resulting in the view-unaligned issue. To tackle this issue, we concept...

Learning-based hashing has received increasing research attention due to its promising efficiency for large-scale similarity search. However, most existing manifold-based hashing methods cannot capture the intrinsic structure and discriminative information of image samples. In this paper, we propose a new learning-based hashing method, namely, Spar...

With the growing interest in convex and nonconvex low-rank matrix learning problems, the widely used singular value thresholding (SVT) operators associated with rank relaxation functions often face higher computational complexity, particularly for large-scale data matrices. To improve the efficacy of low-rank subspace clustering and overcome the is...

There is a large volume of incomplete multi-view data in the real-world. How to partition these incomplete multi-view data is an urgent realistic problem since almost all of the conventional multi-view clustering methods are inapplicable to cases with missing views. In this paper, a novel graph learning-based incomplete multi-view clustering (IMVC)...

Incomplete multiview clustering (IMC) is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multiview data. To date, existing IMC methods usually bypass unavailable views according to prior missing information, which is considered a second-best scheme based on evasion. Other...

In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection and manual annotation, which means that not only multi-view features are often missing, and label completeness is also difficult to...

Diabetic retinopathy (DR) is the main cause of irreversible blindness for working-age adults. The previous models for DR detection have difficulties in clinical application. The main reason is that most of the previous methods only use single-view data, and the single field of view (FOV) only accounts for about 13% of the FOV of the retina, resulti...

Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore the important information hidden in the missing views and the latent intrinsic structures in each view. To tackle these issues, in this paper, a unified and novel framework, named tensorized incomplete m...

As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable for various pattern recognition tasks. In this complex representation learning problem, three main challenges can be characterized...

Palmprint recognition provides a potential solution for noninvasive personal authentication due to its excellent contactless property and user-security, and it has attracted tremendous research interest in recent years. However, most existing methods focus on intraspectral palmprint recognition, which requires gallery and probe images to be capture...

Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is considered as a second-best sch...

View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery. In the past years, many efforts have been made to address the incomplete multi-view learning or incomplete multi-label learning problem. However, few works can simultaneously handle the challenging case with both the inc...

As a cross-topic of multi-view learning and multi-label classification, multi-view multi-label classification has gradually gained traction in recent years. The application of multi-view contrastive learning has further facilitated this process, however, the existing multi-view contrastive learning methods crudely separate the so-called negative pa...

Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be...

Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single-view clustering, MVC improves clustering performance by exploiting complementary and...

Palmprint recognition has recently attracted broad attention due to its rich discriminative features, contactless collection manner and less invasive. However, most existing methods focus on within-illumination palmprint recognition, which requires the similar illumination of query samples acquisition as the gallery samples, significantly limiting...

In recent years, many incomplete multi-view clustering methods have been proposed to address the challenging and new clustering task on incomplete multi-view data whose part of view representations are not fully collected for some samples. Although extensive experiments have validated the effectiveness of these methods for handling the incomplete l...

Weakly supervised video anomaly detection is generally formulated as a multiple instance learning (MIL) problem, where an anomaly detector learns to generate frame-level anomaly scores under the supervision of MIL-based video-level classification. However, most previous works suffer from two drawbacks: 1) they lack ability to model temporal relatio...

Weakly supervised object detection (WSOD) has received widespread attention since it requires only image-category annotations for detector training. Many advanced approaches solve this problem by a two-phase learning framework, that is, instance mining that classifies generated proposals via multiple instance learning, and instance refinement that...

Low-quality palmprint images will degrade the recognition performance, when they are captured under the open, unconstraint, and low-illumination conditions. Moreover, the traditional single-view palmprint representation methods have been difficult to express the characteristics of each palm strongly, where the palmprint characteristics become weak....

In order to reduce the negative effect of missing data on clustering, incomplete multiview clustering (IMVC) has become an important research content in machine learning. At present, graph-based methods are widely used in IMVC, but these methods still have some defects. First, some of the methods overlook potential relationships across views. Secon...

Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. However, most previous works assumed that each view is complete and aligned. This leads to an inevitable deterioration in their performance when encountering practical problems such as missing or unaligned views. To address the challe...

Palmprint recognition has attracted widespread attention because of its advantages such as easy acquisition, rich texture, and security. However, most existing palmprint recognition methods focus most on feature extraction and matching without evaluating the quality of palmprint images, possibly leading to low recognition efficiency. In this paper,...

Preserving the intrinsic structure of data is very important for unsupervised dimensionality reduction. For structure preserving, graph embedding technique is widely considered. However, most of the existing unsupervised graph embedding based methods cannot effectively preserve the intrinsic structure of data since these methods either use the cons...

Linear discriminant analysis (LDA) as a classical supervised dimensionality reduction method has shown powerful capability in various image classification tasks. The purpose of LDA seeks an optimal linear transformation that maps the original data to a low-dimensional space. Inspired by the fact that the kernel trick can capture the nonlinear simil...

Fatty liver disease is a common disease that causes extra fat storage in an individual's liver. Patients with fatty liver disease may progress to cirrhosis and liver failure, further leading to liver cancer. The prevalence of fatty liver disease ranges from 10%- 30% in many countries. In general, detecting fatty liver requires professional neuroima...

Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to...

Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views o...

With the dramatic increase in the amount of multimedia data, cross-modal similarity retrieval has become one of the most popular yet challenging problems. Hashing offers a promising solution for large-scale cross-modal data searching by embedding the high-dimensional data into the low-dimensional similarity preserving Hamming space. However, most e...

In this article, we propose a new linear regression (LR)-based multiclass classification method, called discriminative regression with adaptive graph diffusion (DRAGD). Different from existing graph embedding-based LR methods, DRAGD introduces a new graph learning and embedding term, which explores the high-order structure information between four...

Therapeutic peptide prediction is critical for drug development and therapeutic therapy. Researchers have developed several computational methods to identify different therapeutic peptide types. However, most computational methods focus on identifying the specific type of therapeutic peptides and fail to accurately predict all types of therapeutic...

Weakly supervised object detection (WSOD) has become an effective paradigm, which requires only class labels to train object detectors. However, WSOD detectors are prone to learn highly discriminative features corresponding to local objects rather than complete objects, resulting in imprecise object localization. To address the issue, designing bac...

Breast cancer accounts for the largest number of patients among all cancers in the world. Intervention treatment for early breast cancer can dramatically extend a woman's 5-year survival rate. However, the lack of public available breast mammography databases in the field of Computer-aided Diagnosis and the insufficient feature extraction ability f...

Latent low-rank representation (LatLRR) is a critical self-representation technique that improves low-rank representation (LRR) by using observed and unobserved samples. It can simultaneously learn the low-dimensional structure embedded in the data space and capture the salient features. However, LatLRR ignores the local geometry structure and can...

Age estimation from face images has attracted much attention due to its favorable of many real-world applications such as video surveillance and social networking. However, most existing studies usually directly extract aging-feature, which ignore the high age-related factors such as race and gender information. In this paper, we propose a joint mu...

Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature extraction. However, general deep learning models cannot achieve very satisfactory classification results on mammographic images because these models are not specifically designed for mammographic images and do not take...

In this paper, we propose a Global-Supervised Contrastive loss and a view-aware-based post-processing (VABPP) method for the field of vehicle re-identification. The traditional supervised contrastive loss calculates the distances of features within the batch, so it has the local attribute. While the proposed Global-Supervised Contrastive loss has n...

Video anomaly detection (VAD) refers to the discrimination of unexpected events in videos. The deep generative model (DGM)-based method learns the regular patterns on normal videos and expects the learned model to yield larger generative errors for abnormal frames. However, DGM cannot always do so, since it usually captures the shared patterns betw...

In this article, we propose a collaborative palmprint-specific binary feature learning method and a compact network consisting of a single convolution layer for efficient palmprint feature extraction. Unlike most existing palmprint feature learning methods, such as deep-learning, which usually ignore the inherent characteristics of palmprints and l...

The goal of multi-view spectral clustering (MVSC) is to explore the intrinsic cluster structures embedded in the multi-view data and group the learned optimal feature embeddings into different clusters based on similarity measurement. Although encouraging improvements have been achieved, when facing the incomplete multi-view data, these MVSC method...

In this paper, we propose an augmentation-free graph contrastive learning framework, namely ACTIVE, to solve the problem of partial multi-view clustering. Notably, we suppose that the representations of similar samples (i.e., belonging to the same cluster) and their multiply views features should be similar. This is distinct from the general unsupe...

Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views o...

Conventional multiview clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to t...

In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cros...

With the diversity of information acquisition, data is stored and transmitted in an increasing number of modalities. Nevertheless, it is not unusual for parts of the data to be lost in some views due to unavoidable acquisition, transmission or storage errors. In this paper, we propose an augmentation-free graph contrastive learning framework to sol...

In the real-world, some views of samples are often missing for the collected multiview data. Faced with the incomplete multiview data, most of the existing clustering methods tended to learn a common graph from the available views, where the hidden information of the absent views was ignored. Furthermore, some methods filled the absent instances wi...

Background Therapeutic peptide prediction is critical for drug development and therapy. Researchers have been studying this essential task, developing several computational methods to identify different therapeutic peptide types. Objective Most predictors are the specific methods for certain peptides. Currently, developing methods to predict the p...

Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the criterion for detecting anomalies. However, this hypothesis cannot be always tenable since the deep AE usu...