Multi-view Information Research Articles

Semi-supervised anomaly traffic detection via multi-frequency reconstruction

Anomaly traffic detection is a crucial issue in cyber-security field. Traditionally, many researchers have approached anomaly traffic detection as a supervised classification problem. However, in real-world scenarios, anomaly network traffic is unpredictable, dynamic, and difficult to collect. To address these challenges, we adopt an anomaly detection setting that trains using only normal traffic data and propose a novel semi-supervised method: Multi-Frequency Reconstruction (MFR). Our approach is driven by a key observation: traffic images often lack explicit patterns and texture features, making accurate modeling of normal traffic difficult. To overcome this limitation, we employ low-pass filters to extract multi-scale low-frequency information, offering a more effective multi-view perspective to identify anomaly patterns. Additionally, we introduce a channel-spatial attention mechanism within reconstruction models to enhance the network’s ability to capture the spatio-temporal features of traffic flows. Ultimately, to adequately leverage multi-view information, we integrate anomaly scores from multi-frequency branches, achieving a more comprehensive anomaly detection. Extensive experiments on three public anomaly traffic detection datasets demonstrate the superior performance of our method, outperforming state-of-the-art approaches by 4.6% and 10.5% in AUROC on the DataCon2020 and CIC-IDS2017 datasets, respectively.

Transformer-based multiview spatiotemporal feature interactive fusion for human action recognition in depth videos

Spatiotemporal feature modeling is the key to human action recognition task. Multiview data is helpful in acquiring numerous clues to improve the robustness and accuracy of feature description. However, multiview action recognition has not been well explored yet. Most existing methods perform action recognition only from a single view, which leads to the limited performance. Depth data is insensitive to illumination and color variations and offers significant advantages by providing reliable 3D geometric information of the human body. In this study, we concentrate on action recognition from depth videos and introduce a transformer-based framework for the interactive fusion of multiview spatiotemporal features, facilitating effective action recognition through deep integration of multiview information. Specifically, the proposed framework consists of intra-view spatiotemporal feature modeling (ISTFM) and cross-view feature interactive fusion (CFIF). Firstly, we project a depth video into three orthogonal views to construct multiview depth dynamic volumes that describe the 3D spatiotemporal evolution of human actions. ISTFM takes multiview depth dynamic volumes as input to extract spatiotemporal features of three views with 3D CNN, then applies self-attention mechanism in transformer to model global context dependency within each view. CFIF subsequently extends self-attention into cross-attention to conduct deep interaction between different views, and further integrates cross-view features together to generate a multiview joint feature representation. Our proposed method is tested on two large-scale RGBD datasets by extensive experiments to demonstrate the remarkable improvement for enhancing the recognition performance.

Three-dimensional reconstruction of a light field based on phase restoration for highly reflective surfaces

This paper proposes, a novel, to our knowledge, phase-restoration-based light field method to achieve 3D reconstruction of highly reflective surfaces. First, a focused light field camera whose angular and spatial resolutions can be adjusted according to the needs has been designed and fabricated to capture 4D light field information. Then, according to the pixel offsets between different sub-aperture images, a phase restoration method based on multi-view complementary information is proposed to restore the missing absolute phase information caused by highlights. Finally, a cubic B-spline curve method is used to directly fit the relationship between absolute phase and coordinates to achieve 3D reconstruction of highly reflective surfaces. The experimental results demonstrate that the proposed method effectively utilizes the multi-view information from the light field to restore missing absolute phase data in the phase unwrapping, ensuring accurate 3D reconstruction of highly reflective surfaces. What is more, our method requires no additional hardware, camera angle calibration, or point cloud fusion, which significantly reduces both hardware complexity and computational demands.

Stack-AVP: A Stacked Ensemble Predictor Based on Multi-view Information for Fast and Accurate Discovery of Antiviral Peptides

AVPs, or antiviral peptides, are short chains of amino acids capable of inhibiting viral replication, preventing viral entry, or disrupting viral membranes. They represent a promising area of research for developing new antiviral therapies due to their potential to target a broad spectrum of viruses, incorporating those resistant to traditional antiviral drugs. However, traditional experimental methods for identifying AVPs are often costly and labour-intensive. Thus far, multiple computational methods have been introduced for the in silico identification of AVPs, but these methods still have certain shortcomings. In this study, we propose a novel stacked ensemble learning framework, termed Stack-AVP, for fast and accurate AVP identification. In Stack-AVP, we investigated heterogeneous prediction models, which were trained with 12 commonly used machine learning algorithms coupled with a wide range of multiple feature encoding schemes. Subsequently, these prediction models were adopted to generate multi-view features providing class information and probability information. Finally, we applied our feature selection method to determine the best feature subset for the construction of the final stacked model. Comparative assessments on the independent test dataset revealed that Stack-AVP surpassed the performance of current state-of-the-art methods, with an accuracy of 0.930, MCC of 0.860, and AUC of 0.975. Furthermore, it was found that our multi-view features exhibited a crucial mechanism to improve the prediction performance of AVPs. To facilitate experimental scientists in performing high-throughput identification of AVPs, the prediction sever Stack-AVP is publicly accessible at https://pmlabqsar.pythonanywhere.com/Stack-AVP.

AppPoet: Large language model based android malware detection via multi-view prompt engineering

Due to the vast array of Android applications, their multifarious functions and intricate behavioral semantics, attackers can adopt various tactics to conceal their genuine attack intentions within legitimate functions. However, numerous learning-based methods suffer from a limitation in mining behavioral semantic information, thus impeding the accuracy and efficiency of Android malware detection. Besides, the majority of existing learning-based methods are weakly interpretive and fail to furnish researchers with effective and readable detection reports. Inspired by the success of the Large Language Models (LLMs) in natural language understanding, we propose AppPoet, a LLM-assisted multi-view system for Android malware detection. Firstly, AppPoet employs a static method to comprehensively collect application features and formulate various observation views. Then, using our carefully crafted multi-view prompt templates, it guides the LLM to generate function descriptions and behavioral summaries for each view, enabling deep semantic analysis of the views. Finally, we collaboratively fuse the multi-view information to efficiently and accurately detect malware through a deep neural network (DNN) classifier and then generate the human-readable diagnostic reports. Experimental results demonstrate that our method achieves a detection accuracy of 97.15% and an F1 score of 97.21%, which is superior to the baseline methods. Furthermore, the case study evaluates the effectiveness of our generated diagnostic reports.

MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information

Migraine is considered one of the debilitating primary headache conditions with an estimated worldwide occurrence of approximately 14–15%, contributing highly to factors responsible for global disability. Calcitonin gene-related peptide (CGRP) is a neuropeptide that plays a crucial role in the pathophysiology of migraines and thus, its inhibition can help relieve migraine symptoms. However, conventional process of CGRP drug development has been laborious and time-consuming with incurred costs exceeding one billion dollars. On the other hand, machine learning (ML)-based approaches that are capable of accurately identifying CGRP inhibitors could greatly facilitate in expediting the discovery of novel CGRP drugs. Therefore, this study proposes a novel and high-accuracy meta-model, namely MetaCGRP, that can precisely identify CGRP inhibitors. To the best of our knowledge, MetaCGRP is the first SMILES-based approach that has been developed to identify CGRP inhibitors without the use of 3D structural information. In brief, we initially employed different molecular representation methods coupled with popular ML algorithms to construct a pool of baseline models. Then, all baseline models were optimized and used to generate multi-view features. Finally, we employed the feature selection method to optimize the multi-view features and determine the best feature subset to enable the construction of the meta-model. Both cross-validation and independent tests indicated that MetaCGRP clearly outperforms several conventional ML classifiers, with accuracies of 0.898 and 0.799 on the training and independent test datasets, respectively. In addition, MetaCGRP in conjunction with molecular docking was utilized to identify five potential natural product candidates from Thai herbal pharmacopoeia and analyze their binding affinity and interactions to CGRP. To facilitate community-wide efforts in expediting the discovery of novel CGRP inhibitors, a user-friendly web server for MetaCGRP is freely available at https://pmlabqsar.pythonanywhere.com/MetaCGRP.

Joint segmentation of tumors in 3D PET-CT images with a network fusing multi-view and multi-modal information

Objective. Joint segmentation of tumors in positron emission tomography-computed tomography (PET-CT) images is crucial for precise treatment planning. However, current segmentation methods often use addition or concatenation to fuse PET and CT images, which potentially overlooks the nuanced interplay between these modalities. Additionally, these methods often neglect multi-view information that is helpful for more accurately locating and segmenting the target structure. This study aims to address these disadvantages and develop a deep learning-based algorithm for joint segmentation of tumors in PET-CT images.Approach. To address these limitations, we propose the Multi-view Information Enhancement and Multi-modal Feature Fusion Network (MIEMFF-Net) for joint tumor segmentation in three-dimensional PET-CT images. Our model incorporates a dynamic multi-modal fusion strategy to effectively exploit the metabolic and anatomical information from PET and CT images and a multi-view information enhancement strategy to effectively recover the lost information during upsamping. A Multi-scale Spatial Perception Block is proposed to effectively extract information from different views and reduce redundancy interference in the multi-view feature extraction process.Main results. The proposed MIEMFF-Net achieved a Dice score of 83.93%, a Precision of 81.49%, a Sensitivity of 87.89% and an IOU of 69.27% on the Soft Tissue Sarcomas dataset and a Dice score of 76.83%, a Precision of 86.21%, a Sensitivity of 80.73% and an IOU of 65.15% on the AutoPET dataset.Significance. Experimental results demonstrate that MIEMFF-Net outperforms existing state-of-the-art models which implies potential applications of the proposed method in clinical practice.

Logical Relation Inference and Multiview Information Interaction for Domain Adaptation Person Re-Identification.

Domain adaptation person re-identification (Re-ID) is a challenging task, which aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, some clustering-based domain adaptation Re-ID methods have achieved great success. However, these methods ignore the inferior influence on pseudo-label prediction due to the different camera styles. The reliability of the pseudo-label plays a key role in domain adaptation Re-ID, while the different camera styles bring great challenges for pseudo-label prediction. To this end, a novel method is proposed, which bridges the gap of different cameras and extracts more discriminative features from an image. Specifically, an intra-to-intermechanism is introduced, in which samples from their own cameras are first grouped and then aligned at the class level across different cameras followed by our logical relation inference (LRI). Thanks to these strategies, the logical relationship between simple classes and hard classes is justified, preventing sample loss caused by discarding the hard samples. Furthermore, we also present a multiview information interaction (MvII) module that takes features of different images from the same pedestrian as patch tokens, obtaining the global consistency of a pedestrian that contributes to the discriminative feature extraction. Unlike the existing clustering-based methods, our method employs a two-stage framework that generates reliable pseudo-labels from the views of the intracamera and intercamera, respectively, to differentiate the camera styles, subsequently increasing its robustness. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods. The source code has been released at https://github.com/lhf12278/LRIMV.

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning

Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view information of the input sample to obtain its accurate predictions of multiple labels. Unfortunately, a majority of existing MVML methods are based on the assumption of data completeness, making them useless in practical applications with partially missing views or some uncertain labels. Recently, many approaches have been proposed for incomplete data, but few of them can handle the case of both missing views and labels. Moreover, these few existing works commonly ignore potentially valuable information about unknown labels or do not sufficiently explore latent label information. Therefore, in this paper, we propose a label semantic-guided contrastive learning method named LSGC for the dual incomplete multi-view multi-label classification problem. Concretely, LSGC employs deep neural networks to extract high-level features of samples. Inspired by the observation of exploiting label correlations to improve the feature discriminability, we introduce a graph convolutional network to effectively capture label semantics. Furthermore, we introduce a new sample-label contrastive loss to explore the label semantic information and enhance the feature representation learning. For missing labels, we adopt a pseudo-label filling strategy and develop a weighting mechanism to explore the confidently recovered label information. We validate the framework on five standard datasets and the experimental results show that our method achieves superior performance in comparison with the state-of-the-art methods.

Cross-view discrepancy-dependency network for volumetric medical image segmentation

The limited data poses a crucial challenge for deep learning-based volumetric medical image segmentation, and many methods have tried to represent the volume by its subvolumes (i.e., multi-view slices) for alleviating this issue. However, such methods generally sacrifice inter-slice spatial continuity. Currently, a promising avenue involves incorporating multi-view information into the network to enhance volume representation learning, but most existing studies tend to overlook the discrepancy and dependency across different views, ultimately limiting the potential of multi-view representations. To this end, we propose a cross-view discrepancy-dependency network (CvDd-Net) to task with volumetric medical image segmentation, which exploits multi-view slice prior to assist volume representation learning and explore view discrepancy and view dependency for performance improvement. Specifically, we develop a discrepancy-aware morphology reinforcement (DaMR) module to effectively learn view-specific representation by mining morphological information (i.e., boundary and position of object). Besides, we design a dependency-aware information aggregation (DaIA) module to adequately harness the multi-view slice prior, enhancing individual view representations of the volume and integrating them based on cross-view dependency. Extensive experiments on four medical image datasets (i.e., Thyroid, Cervix, Pancreas, and Glioma) demonstrate the efficacy of the proposed method on both fully-supervised and semi-supervised tasks.

Stack-HDAC3i: A high-precision identification of HDAC3 inhibitors by exploiting a stacked ensemble-learning framework

Epigenetics involves reversible modifications in gene expression without altering the genetic code itself. Among these modifications, histone deacetylases (HDACs) play a key role by removing acetyl groups from lysine residues on histones. Overexpression of HDACs is linked to the proliferation and survival of tumor cells. To combat this, HDAC inhibitors (HDACi) are commonly used in cancer treatments. However, pan-HDAC inhibition can lead to numerous side effects. Therefore, isoform-selective HDAC inhibitors, such as HDAC3i, could be advantageous for treating various medical conditions while minimizing off-target effects. To date, computational approaches that use only the SMILES notation without any experimental evidence have become increasingly popular and necessary for the initial discovery of novel potential therapeutic drugs. In this study, we develop an innovative and high-precision stacked-ensemble framework, called Stack-HDAC3i, which can directly identify HDAC3i using only the SMILES notation. Using an up-to-date benchmark dataset, we first employed both molecular descriptors and Mol2Vec embeddings to generate feature representations that cover multi-view information embedded in HDAC3i, such as structural and contextual information. Subsequently, these feature representations were used to train baseline models using nine popular ML algorithms. Finally, the probabilistic features derived from the selected baseline models were fused to construct the final stacked model. Both cross-validation and independent tests showed that Stack-HDAC3i is a high-accuracy prediction model with great generalization ability for identifying HDAC3i. Furthermore, in the independent test, Stack-HDAC3i achieved an accuracy of 0.926 and Matthew’s correlation coefficient of 0.850, which are 0.44–6.11% and 0.83–11.90% higher than its constituent baseline models, respectively.

Multi-stakeholder recommendation system through deep learning-based preference evaluation and aggregation model with multi-view information embedding

Learning the preferences of consumers, providers, and system stakeholders is a challenging problem in the Multi-Stakeholder Recommendation System (MSRS). Existing MSRS methods lack the ability to generate equitable recommendations and investigate implicit relationships between stakeholders and items. Hence, this study addresses this issue by proposing a multi-stakeholder preference learning-based recommendation model that exploits information from multiple views to evaluate stakeholders' preferences. The proposed model learns consumer preferences using users' ratings and reviews of an item, provider preferences with provider utility, and provider-item interaction. Furthermore, the proposed model learns the system-level preference of promoting long-tail items through the probabilistic evaluation of stakeholders' interest in popular and unpopular items. Finally, this study develops a multi-stakeholder, multi-view deep neural network model to aggregate stakeholders' preferences and deliver equitable recommendations. This work utilizes benchmark Movie Lens (ML) 25M, ML-100K, ML-1M, and TripAdvisor datasets to validate and compare the proposed model's performance with other baseline methods using standard evaluation metrics for each stakeholder. Examining the precision metrics, the proposed model attains the minimum enhancement of 7.91%, 18.24%, 10.72%, and 20.12% across the ML-25M, ML-100K, ML-1M, and TripAdvisor datasets. Further concerning the exposure, hit, and reach metrics, the model exhibits a substantial minimum improvement of 19.12%, 14.73%, 5.37%, and 28.46% over the ML-25M, ML-100K, ML-1M, and TripAdvisor datasets. Finally, the proposed model excels in promoting long-tail items and enhancing the cumulative utility gain of the stakeholders, surpassing the baseline methods.

Evidential dissonance measure in robust multi-view classification to resist adversarial attack

Multi-view learning is effective in improving data classification accuracy through integrating information from multiple sources. To guarantee the reliability of multi-view classification, the trusted multi-view classification methods have been explored. However, extant trusted multi-view classification methods are still vulnerable to the low-quality views containing adversarial samples. This vulnerability arises from a challenge in accurately assessing the quality of data views that are subjected to adversarial attacks. To address this issue, we propose a robust multi-view classification method with dissonance measure of adversarial samples. Specifically, the proposed method utilizes the evidential dissonance measure in subjective logic to assess the quality of data views when encountering adversarial attacks. Based on the dissonance measure, we further propose a strategy of dissonance-aware belief integration for multi-view information fusion and construct the inter-view evidential gradient penalty in the learning objective of multi-view classification to improve the model robustness to resist adversarial samples. Experiments on diverse multi-view datasets confirm the reliability and robustness of the proposed multi-view classification method.

Incomplete Multiview Clustering Based on Consensus Information.

In contrast to traditional single-view clustering methods, multiview clustering (MVC) approaches aim to extract, analyze, and integrate structural information from diverse perspectives, providing a more comprehensive understanding of internal data structures. However, with an increasing number of views, maintaining the integrity of view information becomes challenging, giving rise to incomplete MVC (IMVC) methods. While existing IMVC methods have shown notable performance on many incomplete multiview (IMV) databases, they still grapple with two key shortcomings: 1) they treat the information of each view as a whole, disregarding the differences among samples within each view; and 2) they rely on eigenvalue and eigenvector operations on the view matrix, limiting their scalability for large-scale samples and views. To overcome these limitations, we propose a novel multiview clustering with consistent information (IMVC-CI) of sample points. Our method explores the multiview information set of sample points to extract consensus structural information and subsequently restores unknown information in each view. Importantly, our approach operates independently on individual sample points, eliminating the need for eigenvalue and eigenvector operations on the view information matrix and facilitating parallel computation. This significantly enhances algorithmic efficiency and mitigates challenges associated with dimensionality. Experimental results on various public datasets demonstrate that our algorithm outperforms state-of-the-art IMVC methods in terms of clustering performance and computational efficiency. The code for our article has been uploaded to https://github.com/PhdJiayiTang/IMVC-CI.git.

Query-Based Multiview Detection for Multiple Visual Sensor Networks.

In IoT systems, the goal of multiview detection for multiple visual sensor networks is to use multiple camera perspectives to address occlusion challenges with multiview aggregation being a crucial component. In these applications, data from various interconnected cameras are combined to create a detailed ground plane feature. This feature is formed by projecting convolutional feature maps from multiple viewpoints and fusing them using uniform weighting. However, simply aggregating data from all cameras is not ideal due to different levels of occlusion depending on object positions and camera angles. To overcome this, we introduce QMVDet, a new query-based learning multiview detector, which incorporates an innovative camera-aware attention mechanism for aggregating multiview information. This mechanism selects the most reliable information from various camera views, thus minimizing the confusion caused by occlusions. Our method simultaneously utilizes both 2D and 3D data while maintaining 2D-3D multiview consistency to guide the multiview detection network's training. The proposed approach achieves state-of-the-art accuracy on two leading multiview detection benchmarks, highlighting its effectiveness for IoT-based multiview detection scenarios.

Multi-Hop Multi-View Memory Transformer for Session-Based Recommendation

A Session-Based Recommendation (SBR) seeks to predict users’ future item preferences by analyzing their interactions with previously clicked items. In recent approaches, Graph Neural Networks (GNNs) have been commonly applied to capture item relations within a session to infer user intentions. However, these GNN-based methods typically struggle with feature ambiguity between the sequential session information and the item conversion within an item graph, which may impede the model’s ability to accurately infer user intentions. In this article, we propose a novel Multi-hop Multi-view Memory Transformer (M 3 T) to effectively integrate the sequence-view information and relation conversion (graph-view information) of items in a session. First, we propose a Multi-view Memory Transformer (M 2 T) module to concurrently obtain multi-view information of items. Then, a set of trainable memory matrices are employed to store sharable item features, which mitigates cross-view item feature ambiguity. To comprehensively capture latent user intentions, an M 3 T framework is designed to integrate user intentions across different hops of an item graph. Specifically, a k-order power method is proposed to manage the item graph to alleviate the over-smoothing problem when obtaining high-order relations of items. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our method.

Automatic liver segmentation from CT volumes based on multi-view information fusion and condition random fields

Liver segmentation from CT images is a fundamental and essential step for many clinical applications, such as disease diagnosis, surgery navigator, and radiation therapy. Due to the limited computing resources, automatic and accurate liver segmentation from 3D CT volume is still a challenge task. In this paper, we propose an automatic liver segmentation method by fusing multi-view information. First, a 2D network combined with Dual Self-Attention (DSA) is proposed and applied on multi-sectional 2D slices reconstructed from axial, coronal, and sagittal directions. Then, to generate 3D segmentation results, a lightweight 3D network is designed to fuse the segmentation results from different viewing. Finally, a full connection condition random field is used to refine the 3D results. The proposed method is evaluated on four public datasets, including 3DIRCADb, CHAOS, Sliver07, and LiTS. The experimental results show that the proposed method can accurately segment the liver region for the datasets with large or small cases, as well as healthy or lesion livers. The dice values obtained by the proposed method on four datasets are 0.952, 0.969, 0.958, and 0.964, respectively, which are better than those obtained by many existing ones.

End-to-end multiview fusion for building mapping from aerial images

In the domain of photogrammetry, the fusion of information from multiple views holds the potential to significantly enhance the accuracy and robustness of building mapping. While multiview observation and stereoscopic imaging form the bedrock of photogrammetric projects, current deep learning methodologies predominantly focus on orthophotos and digital surface models (DSMs), often sidelining the rich multiview information inherent in original images. Addressing this gap, we present Multiview Mapper (MVMapper), an end-to-end learning framework explicitly crafted to harness and fuse the rich semantic information from original multiview images with object-space features. MVMapper uses stereo labels for supervised building segmentation in a dual-space, encompassing both image and object domains. Additionally, it incorporates a novel piecewise affine projection method for ensuring a robust image-to-object feature transformation. Experimental results on an aerial photogrammetric dataset with a resolution of 30 cm demonstrate MVMapper’s superiority over state-of-the-art multiview data fusion methods, yielding significant improvements in segmentation and contour accuracy. Notably, the proposed piecewise affine projection method mitigates misalignment issues caused by DSM noise, enabling the effective fusion of multiview features with object-space features. Further experimentation on a separate open-source dataset demonstrates MVMapper’s substantial advantages in transferability to other contexts.

View-interactive attention information alignment-guided fusion for incomplete multi-view clustering

Multi-view data from real applications is usually incomplete, leading to unsatisfactory performance in clustering tasks. Existing incomplete multi-view clustering (IMVC) methods still encounter the following issues: (1) They focus on learning consistency features between different views but often struggle to recover missing data based on the complementary features between views. (2) Differing from complete multi-view data, the misaligned characteristics of incomplete multi-view data may introduce noises in feature learning, thereby affecting the performance of the IMVC models. To address these issues, we propose a novel view-interactive attention information alignment-guided fusion (VAIAF) method for clustering incomplete multi-view data. Firstly, it utilizes an autoencoder to extract feature information from each view. Then we construct an information interactive attention (IIA) module to learn multi-view information by introducing the self-attention mechanism. This module not only extracts consistent features from different views but also utilizes the feature complementary relationships between views to recover missing data to a certain extent. Therefore, it can enhance the capability of the model to learn the consistency and complementarity of incomplete multi-view data. The proposed VAIAF method also constructs a discriminator for each view to facilitate missing data recovery. In the feature fusion stage, we implement an alignment-guided strategy to prevent noise caused by misaligned feature fusion. Extensive experiments on four incomplete multi-view datasets demonstrate the effectiveness of the proposed VAIAF method in clustering tasks.

Multi-task contrastive learning for change detection in remote sensing images

ABSTRACT This letter proposes a novel multi-task contrastive learning (MTCL) approach for change detection of high-resolution remote sensing images. Current self-supervised learning approaches have limitations in utilizing multiview information of remote sensing images, and they cannot directly train the change detection backbone network for feature learning. The investigated method exploits multiview information contained in remote sensing images to directly train the change detection backbone network. Specifically, multiview views are constructed from high-resolution remote sensing images by handcrafted feature extraction approach, which contain complementary information from different domains. Then each constructed view is utilized for contrastive learning, and the multi-task learning strategy of parameter sharing is used to learn more rich and robust information. Furthermore, instead of learning feature representations from single remote sensing image, the proposed approach directly utilizes image pairs to train the change detection backbone, further improving the performance of change detection network. Two benchmark datasets are employed for comparative experiments, and experimental results certify the effectiveness and superiority of the investigated approach.