Data Augmentation Strategy Research Articles

A Picture May Be Worth a Hundred Words for Visual Question Answering

How far can textual representations go in understanding images? In image understanding, effective representations are essential. Deep visual features from object recognition models currently dominate various tasks, especially Visual Question Answering (VQA). However, these conventional features often struggle to capture image details in ways that match human understanding, and their decision processes lack interpretability. Meanwhile, the recent progress in language models suggests that descriptive text could offer a viable alternative. This paper investigated the use of descriptive text as an alternative to deep visual features in VQA. We propose to process description–question pairs rather than visual features, utilizing a language-only Transformer model. We also explored data augmentation strategies to enhance training set diversity and mitigate statistical bias. Extensive evaluation shows that textual representations using approximately a hundred words can effectively compete with deep visual features on both the VQA 2.0 and VQA-CP v2 datasets. Our qualitative experiments further reveal that these textual representations enable clearer investigation of VQA model decision processes, thereby improving interpretability.

Multi-vendor validation of automated left ventricular analysis from 3D echocardiography using artificial intelligence

Abstract Background Accurate echocardiographic assessment of left ventricular (LV) function is dependent on both image acquisition and analysis. Although 3D echocardiography (3DE) is increasingly recommended for volume quantification, manually derived volumes are limited by operator subjectivity, variable image quality, and the lengthy duration of analysis. Consequently, several automated approaches based on artificial intelligence (AI) have been proposed to segment the LV cavity from 3DE and have demonstrated better accuracy and reproducibility compared to human experts. While the utility of AI models in the clinic depends on their ability to generalise to different populations and ultrasound vendors, the external validation of developed models remains limited. Purpose This study evaluates the ability of an existing AI model trained using data from a single vendor to accurately estimate routine 3DE LV indices in an independent external dataset acquired using equipment from a different vendor. Methods A previously published and internally validated deep learning model for segmentation of the LV cavity and myocardium from 3DE was used to automatically derive LV indices including end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and ejection fraction (EF) [1]. A second independent dataset consisting of paired 3DE and CMR images from 65 marathon runners from a different centre (using equipment from a different vendor) was subsequently used for external validation. Images from both datasets were subjectively scored from 1 (poor) to 5 (excellent) as a measure of image quality. Agreement with CMR as the reference was assessed using a two-way mixed effects intraclass correlation coefficient (ICC), supplemented by Bland-Altman analysis, to determine the model bias (between 3DE and CMR) and 95% limits of agreement (LOA). To assess whether performance could be improved by inclusion of multi-vendor data during training, the initial model was fine-tuned using a small subset (n=9) of images from the second centre. Results The mean image quality score from the second dataset was 2.5 (± 1.1), which was significantly lower than the mean of 3.4 (± 1.0) from the first dataset. Despite vendor and image quality differences, the initial model produced good agreement with CMR in EDV, ESV, and LV mass but only poor agreement in EF (Fig 1). Fine tuning greatly improved agreement in EF with reduced bias and narrower 95% LOA (Fig 2). Conclusion With the addition of a small number of cases for fine-tuning, AI models for automated LV analysis can achieve good performance analysing 3DE images acquired from a different vendor and population. Advancing model generalisability through more sophisticated data augmentation strategies may enable truly vendor-agnostic automated LV analysis from 3DE.

Intelligent Manufacturing in Wine Barrel Production: Deep Learning-Based Wood Stave Classification

Innovative wood inspection technology is crucial in various industries, especially for determining wood quality by counting rings in each stave, a key factor in wine barrel production. (1) Background: Traditionally, human inspectors visually evaluate staves, compensating for natural variations and characteristics like dirt and saw-induced aberrations. These variations pose significant challenges for automatic inspection systems. Several techniques using classical image processing and deep learning have been developed to detect tree-ring boundaries, but they often struggle with woods exhibiting heterogeneity and texture irregularities. (2) Methods: This study proposes a hybrid approach combining classical computer vision techniques for preprocessing with deep learning algorithms for classification, designed for continuous automated processing. To enhance performance and accuracy, we employ a data augmentation strategy using cropping techniques to address intra-class variability in individual staves. (3) Results: Our approach significantly improves accuracy and reliability in classifying wood with irregular textures and heterogeneity. The use of explainable AI and model calibration offers a deeper understanding of the model’s decision-making process, ensuring robustness and transparency, and setting confidence thresholds for outputs. (4) Conclusions: The proposed system enhances the performance of automatic wood inspection technologies, providing a robust solution for industries requiring precise wood quality assessment, particularly in wine barrel production.

Deep learning-powered efficient characterization and quantification of microplastics

Characterizing and quantifying microplastics (MPs) are time-consuming and labor-intensive tasks traditionally. This paper presents an artificial intelligence (AI) framework aiming to automate these tasks by integrating computer vision and deep learning techniques. The approach leverages Fourier Transform Infrared (FTIR) spectra and visual images. Primary novelties of this research involve the development of: (1) an AI framework integrating efforts of data processing, analytics, visualization, and human-computer interaction; (2) a method for transforming FTIR data into contour images; (3) data augmentation strategies for resolving data scarcity and imbalance issues; (4) deep learning models for identifying MPs; (5) computer vision algorithms for quantifying MPs; and (6) an engineer-friendly graphic user interface (GUI) for enhancing data accessibility. The AI framework has been applied to polyethylene, polypropylene, polystyrene, polyamide, ethylene-vinyl acetate, and cellulose acetate. Results confirmed the efficacy of the framework, exhibiting high accuracy scores in classification (98 %), segmentation (99 %), and quantification (96 %) tasks. This research advances the capability of automatic assessment of MPs.

Enhanced ANN-based ensemble method for bridge damage characterization using limited dataset

Bridges are vital assets of transport infrastructure, systems, and communities. Damage characterization is critical in ensuring safety and planning adaptation measures. Nondestructive methods offer an efficient means towards assessing the condition of bridges, without causing harm or disruption to transport services, and these can deploy measurable evidence of bridge deterioration, e.g., deflections due to tendon loss. This paper presents an enhanced input-doubling technique and the Artificial Neural Network (ANN)-based cascade ensemble method for bridge damage state identification and is exclusively relying on small datasets, that are common in structural assessments. A new data augmentation scheme rooted in the principles of linearizing response surfaces is introduced, which significantly boosts the efficiency of intelligent data analysis when faced with limited volumes of data. Furthermore, improvements to a two-step ANN-based ensemble method, designed for solving the stated task, are presented. By adding the improved input-doubling methods as simple predictors in the first part of the cascade ensemble and optimizing it, we significantly boost accuracy (7%, 0.5%, and 8% based on R2 in predicting tendon losses for three critical zones that were defined across the deck of a real deteriorated prestressed balanced cantilever bridge). This improvement is strong evidence of the accuracy of the proposed method for the task at hand that is proven to be more accurate than other methods available in the international literature.

Assessment of Computer-Aided Translation Quality Based on Large-Scale Corpora

CAT technology, utilizing translation memory and quality control tools, boosts translation efficiency and consistency. Yet, it faces challenges with cultural nuances, context, and creativity, requiring human intervention. This study explores leveraging large-scale corpora to enhance machine translation quality assessment accuracy and efficiency. Automated evaluation models surpass manual limitations, enhancing translation system performance and decision-making. Supervised and unsupervised machine translation quality assessment methods, coupled with data augmentation strategies, reduce model bias by reinforcing the connection between source and target languages. The proposed model achieves 94.8% and 92.6% correctness on Google Translate and Wikipedia datasets, slightly surpassing manual rates. Data augmentation and cascade evaluation notably improve translation evaluation accuracy, especially with large-scale corpora. Future research targets higher-quality pseudo-labeled data, refined evaluation models, and advanced assessment methods for diverse translation needs.

Improving the classification of rock samples with diffusion probabilistic machine learning

Machine learning is a powerful data-driven technology for regression and classification tasks in numerous branches of science and technology. With probabilistic machine learning, we can approximate the probability density function of a given digital data set, unlocking the sampling of new and realistic data. We implement probabilistic machine learning as a statistical-based data augmentation tool to effectively improve the performance of a classifier on scarce data sets. Limited data are the norm in most practical situations, as is the case with core samples retrieved from unconventional oil and gas reservoirs. Obtaining and properly labeling rock sample images is a manual and time-consuming task. This leads to scarce data sets that make the lithologic classification of rock samples challenging. We illustrate the technology on images taken from well cores from the Argentinean Neuquén and Austral basins, where by introducing generated samples in the training stage of a machine-learning classifier, we achieve a 5% higher accuracy with respect to the baseline obtained using the same number of samples from the original data set. We reach these results by proposing a lightweight workflow that can be run on very limited hardware. Our workflow is an alternative to classical data augmentation strategies for dealing with incomplete information, such as geometric transformations.

Data-augmented machine learning scoring functions for virtual screening of YTHDF1 m6A reader protein

Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and efficiency. Innovation in computer-aided drug design is primarily driven by structure- and ligand-based approaches. When the number of known inhibitors for a target is limited, data augmentation strategies are often preferred to enhance model performance. In this study, we developed predictive machine learning models for structure-based drug discovery leveraging multiple traditional machine learning algorithms trained with target and ligand dynamics-aware datasets.To illustrate our approach, we present a composite model that combines classification and regression to predict YTHDF1 inhibitors, utilizing PLEC features. YTHDF1, a key m6A reader protein involved in mRNA translation, is implicated in various cancers, making it a promising therapeutic target. Traditional structure-based virtual screening (SBVS) using generic scoring functions has struggled to identify potent YTHDF1 inhibitors due to the protein's unique binding characteristics. To overcome this, we developed YTHDF1-specific machine learning scoring functions (MLSFs) to enhance SBVS efficacy.We employed various data augmentation techniques to generate a comprehensive dataset, incorporating multiple conformations of ligands and the YTHDF1 protein. We have trained 64 YTHDF1-specific MLSFs using four machine learning algorithms and evaluated them on ten test sets, focusing on their predictive and ranking power. Our results demonstrate that the artificial neural network with protein-ligand extended connectivity fingerprints (ANN-PLEC) outperforms other MLSFs, consistently achieving high area under the precision-recall curve (PR-AUC) of 0.87. This method shows promise for targets with limited quantities of active molecules, providing a viable path forward for drug discovery research. The ANN-PLEC scoring function is made freely available on GitHub for other researchers to access and utilize https://github.com/JuniML/SBVS-YTHDF1/.

Enabling personalized smart tourism with location-based social networks

With the rapid advance of mobile internet, communication technology and the Internet of Things (IoT), the tourism industry is undergoing unprecedented transformation. Smart tourism offers users personalized and customized services for travel planning and recommendations. Location-based social networks (LBSNs) play a crucial role in smart tourism industry by providing abundant data sources through their social networking attributes. However, applying LBSNs to smart tourism is a challenge due to the need to deal with complex multi-source information modeling and tourism data sparsity. In this article, to fully harness the potential of LBSNs using deep learning technologies, we propose an knowledge-driven personalized recommendation method for smart tourism. Representation learning techniques can effectively modeling the contextual information (e.g., time, space, and semantics) in LBSNs, while the data augmentation strategy of contrastive learning techniques can explore user personalized travel behaviors and alleviate data sparsity. To demonstrate the effectiveness of the proposed approach, we conducted a case study on trip recommendation. Furthermore, the patterns of human mobility are revealed by exploring the effect of contextual data and tourist potential preferences.

Multi-Scale Frequency-Adaptive-Network-Based Underwater Target Recognition

Due to the complexity of underwater environments, underwater target recognition based on radiated noise has always been challenging. This paper proposes a multi-scale frequency-adaptive network for underwater target recognition. Based on the different distribution densities of Mel filters in the low-frequency band, a three-channel improved Mel energy spectrum feature is designed first. Second, by combining a frequency-adaptive module, an attention mechanism, and a multi-scale fusion module, a multi-scale frequency-adaptive network is proposed to enhance the model’s learning ability. Then, the model training is optimized by introducing a time–frequency mask, a data augmentation strategy involving data confounding, and a focal loss function. Finally, systematic experiments were conducted based on the ShipsEar dataset. The results showed that the recognition accuracy for five categories reached 98.4%, and the accuracy for nine categories in fine-grained recognition was 88.6%. Compared with existing methods, the proposed multi-scale frequency-adaptive network for underwater target recognition has achieved significant performance improvement.

Bimodal biometric recognition system using Convolutional Neural Networks and fusion of deep audiovisual feature vectors

In recent years, interest has grown in the use biometric systems for identity authentication tasks in digital services, forensic and security applications. A unimodal system (employing a single biometric trait) with high performance is still vulnerable to falsification attacks such as spoofing. For this reason, research on multimodal biometrics (employing various biometric traits) has increased to reinforce security, increase recognition performance, and make false identity authentication more difficult. In this paper, we propose a bimodal system that combines speech and face modalities by concatenating their feature vectors, these vectors are extracted from two convolutional neural networks (CNN) and used for identity verification. The performance of unimodal CNNs was evaluated individually and compared to the bimodal system of concatenated vectors. A data augmentation scheme is used for both modalities to evaluate different operation conditions. Results were measured in terms of Equal Error Rate (EER).

Strategic data augmentation with CTGAN for smart manufacturing: Enhancing ML predictions of paper breaks in pulp-and-paper production

This article addresses the significant challenge posed by the rare occurrence of paper breaks in the pulp-and-paper industry, which, despite their infrequency during production, have considerable economic consequences. We analyze operational data from a paper manufacturing machine, focusing on the rare instances of paper breaks (124 out of 18,398 cases) identified through a quality assurance protocol. This scarcity presents a challenge for machine learning-based predictive models. To overcome this, we introduce a novel data augmentation strategy using Conditional Generative Adversarial Networks (CTGAN) and Synthetic Minority Oversampling Technique (SMOTE), aimed at generating synthetic data that not only reflects the real operational data’s distribution but also improves the predictive models’ performance metrics. We assess the impact of this data augmentation on three machine learning algorithms—Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR)—both before and after augmentation. The use of a CTGAN-enhanced dataset led to notable enhancements in predictive maintenance metrics. Specifically, the ability of the models to detect machine breaks (Class 1) increased significantly: over 30% for Decision Trees, 20% for Random Forest, and nearly 90% for Logistic Regression. This methodological progress offers valuable contributions to the fields of industrial quality control and maintenance scheduling by improving the prediction of rare events in manufacturing processes.

COMPARATIVE ANALYSIS OF MULTILINGUAL QA MODELS AND THEIR ADAPTATION TO THE KAZAKH LANGUAGE

This paper presents a comparative analysis of large pretrained multilingual models for question-answering (QA) systems, with a specific focus on their adaptation to the Kazakh language. The study evaluates models including mBERT, XLM-R, mT5, AYA, and GPT, which were tested on QA tasks using the Kazakh sKQuAD dataset. To enhance model performance, fine-tuning strategies such as adapter modules, data augmentation techniques (back-translation, paraphrasing), and hyperparameter optimization were applied. Specific adjustments to learning rates, batch sizes, and training epochs were made to boost accuracy and stability. Among the models tested, mT5 achieved the highest F1 score of 75.72%, showcasing robust generalization across diverse QA tasks. GPT-4-turbo closely followed with an F1 score of 73.33%, effectively managing complex Kazakh QA scenarios. In contrast, native Kazakh models like Kaz-RoBERTa showed improvements through fine-tuning but continued to lag behind larger multilingual models, underlining the need for additional Kazakh-specific training data and further architectural enhancements. Kazakh’s agglutinative morphology and the scarcity of high-quality training data present significant challenges for model adaptation. Adapter modules helped mitigate computational costs, allowing efficient fine-tuning in resource-constrained environments without significant performance loss. Data augmentation techniques, such as back-translation and paraphrasing, were instrumental in enriching the dataset, thereby enhancing model adaptability and robustness. This study underscores the importance of advanced fine-tuning and data augmentation strategies for QA systems tailored to low-resource languages like Kazakh. By addressing these challenges, this research aims to make AI technologies more inclusive and accessible, offering practical insights for improving natural language processing (NLP) capabilities in underrepresented languages. Ultimately, these findings contribute to bridging the gap between high-resource and low-resource language models, fostering a more equitable distribution of AI solutions across diverse linguistic contexts.

Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks.

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.

ML-GAP: machine learning-enhanced genomic analysis pipeline using autoencoders and data augmentation.

The advent of RNA sequencing (RNA-Seq) has significantly advanced our understanding of the transcriptomic landscape, revealing intricate gene expression patterns across biological states and conditions. However, the complexity and volume of RNA-Seq data pose challenges in identifying differentially expressed genes (DEGs), critical for understanding the molecular basis of diseases like cancer. We introduce a novel Machine Learning-Enhanced Genomic Data Analysis Pipeline (ML-GAP) that incorporates autoencoders and innovative data augmentation strategies, notably the MixUp method, to overcome these challenges. By creating synthetic training examples through a linear combination of input pairs and their labels, MixUp significantly enhances the model's ability to generalize from the training data to unseen examples. Our results demonstrate the ML-GAP's superiority in accuracy, efficiency, and insights, particularly crediting the MixUp method for its substantial contribution to the pipeline's effectiveness, advancing greatly genomic data analysis and setting a new standard in the field. This, in turn, suggests that ML-GAP has the potential to perform more accurate detection of DEGs but also offers new avenues for therapeutic intervention and research. By integrating explainable artificial intelligence (XAI) techniques, ML-GAP ensures a transparent and interpretable analysis, highlighting the significance of identified genetic markers.

Perbandingan Kinerja Model Pre-Trained CNN (VGG16, RESNET, dan INCEPTIONV3) untuk Aplikasi Pengenalan Wajah pada Sistem Absensi Karyawan

Face recognition has become a key technology in improving the efficiency and security of modern employee attendance systems. This study compares the performance of three pre-trained Convolutional Neural Network (CNN) models - VGG16, ResNet50, and InceptionV3 - in the context of face recognition for employee attendance systems. The study evaluated the accuracy, consistency, and generalization of the models on a dataset of employee faces, using prediction accuracy, confusion matrix, and classification report measurement methods. Results showed InceptionV3 performed best overall, with high consistency and confidence, achieving up to 99% accuracy on the test data. ResNet50 showed consistent performance in some cases but required further fine-tuning, while VGG16 showed the worst performance. These findings have significant practical implications for the industry, recommending the use of InceptionV3 for the implementation of reliable face recognition-based attendance systems, with consideration of the use of confidence thresholds to optimize accuracy. This research also highlights the importance of further optimization, including hyperparameter fine-tuning and more sophisticated data augmentation strategies, to improve system performance under various work environment conditions.

A deep learning-powered intelligent microdroplet analysis workflow for in-situ monitoring and evaluation of a dynamic emulsion

Accurately determining microdroplet size and uniformity in dispersed phase systems is key for assessing emulsion stability, and essential for quality monitoring and control of emulsion products. Microscopic imaging probes have provided intuitive and advanced insights for characterizing particle microscopic morphology and dispersion in multiphase flows. However, wide size distributions, edge-cutting, overlapping, and high-density target droplets can compromise the image processing accuracy, thereby affecting the precise assessment of emulsion status. In this study, we present a deep learning-based intelligent workflow for microdroplet characterization using an in-situ high-speed particle imaging probe combined with advanced image processing techniques. With the assistance of progressive automatic annotation, a droplet image database was constructed for training the Mask R-CNN model. By incorporating image patching and supervised data augmentation strategies, our well-trained model with accuracy of 95.5% can precisely detect, localize, and segment microscale droplets with various patterns. Additionally, the shape reconstruction module visualizes true shapes of overlapping and edge-cutting droplets, facilitating precise quantification of droplet size information. To validate the feasibility and generalizability of the proposed workflow, we obtained sufficient droplet images through offline sampling during emulsion processes while employing back and front illumination for in-situ monitoring. The automated method achieves precise droplet detection and size measurement, demonstrating its immense potential in online monitoring, evaluation, and control of emulsion quality.

FlexibleCP: A data augmentation strategy for traffic sign detection

AbstractIn the field of traffic sign detection, effective data augmentation can improve the model's detection capacity, enabling the model to distinguish and locate traffic signs more precisely and enhancing driving safety. However, due to the small size and low representation of traffic signs in the dataset, standard common data augmentation techniques are not suitable for traffic sign detection. To address this issue, a novel data augmentation strategy called flexible cut and paste (FlexibleCP) is proposed. The overall enhancement approach is shifted from multi‐image fusion to target cropping and pasting. By introducing parameters to control the target pasting ratio and scaling ratio, the diversity of small target data and their size variations are enriched. Additionally, target size and type filters are added to enable targeted enhancement for different sizes and types of targets. This study, evaluates the proposed strategy using two representative traffic sign detection datasets, namely CTSD and GTSDB. The experimental results demonstrate a significant improvement in both detection and recognition performance of the model: on the CTSD dataset, the models trained with FlexibleCP data enhancement achieve 88.9% and 64.5% mAP0.5 and mAP0.5:0.95, respectively, which are 3.5% and 2.5% better than those trained with mosaic data enhancement; on the GTSDB dataset mAP0.5 and mAP0.5:0.95 reached 89.2% and 56.0%, respectively, an improvement of 4.0% and 3.9% over mosaic.

YOLOv8-Based Drone Detection: Performance Analysis and Optimization

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of drones. The purpose of this study is the evaluation of deep learning approaches such as pre-trained YOLOv8 drone detection for security issues. This study focuses on the YOLOv8 model to achieve optimal performance in object detection tasks using a publicly available dataset collected by Mehdi Özel for a UAV competition that is sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. YOLOv8, known for its advanced architecture, was selected due to its suitability for real-time detection applications and its ability to process complex visual data. Hyperparameter tuning and data augmentation techniques were applied to maximize the performance of the model. Basic hyperparameters such as learning rate, batch size, and optimization settings were optimized through iterative experiments to provide the best performance. In addition to hyperparameter tuning, various data augmentation strategies were used to increase the robustness and generalization ability of the model. Techniques such as rotation, scaling, flipping, and color adjustments were applied to the dataset to simulate different conditions and variations. Among the augmentation techniques applied to the specific dataset in this study, rotation was found to deliver the highest performance. Blurring and cropping methods were observed to follow closely behind. The combination of optimized hyperparameters and strategic data augmentation allowed YOLOv8 to achieve high detection accuracy and reliable performance on the publicly available dataset. This method demonstrates the effectiveness of YOLOv8 in real-world scenarios, while also highlighting the importance of hyperparameter tuning and data augmentation in increasing model capabilities. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605, and a considerable precision–recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.

Knowledge-driven hierarchical intents modeling for recommendation

Previous studies on user–item interaction graphs have typically concentrated on simple interactions, often overlooking the significant role of user intent in shaping these interactions. While some recent research has explored intent relationships to enhance modeling, these approaches mainly focus on user preferences derived from interactions, ignoring the knowledge information with good interpretation in knowledge graphs. In addition, some recent work usually utilize undenoised graph structure information to learn the node representations, which introduces plenty of noise and impedes the well learning of users’ preference. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel knowledge-driven hierarchical intent modeling framework called KHIM. The focus is on designing a hierarchical user intent modeling process and an intent-based multi-view contrastive learning mechanism. The former extracts both the popular and personalized preferences of users from attribute tuples within the knowledge graph at the global-level and local-level, respectively. For global level, we capture the user’s true intents from positive view and augment the positive intent representation with negative view. While the latter generates high-quality user and item representations through multi-level cross-view contrastive learning. Two data augmentation strategies are designed during the contrastive process to mitigate the effects of noise in the learning process. Additionally, we also designed a neighbor filtering strategy based on semantic view to obtain more neighbor semantic information of user and item nodes, so as to further improve the recommendation performance. Experimental results on three benchmark datasets demonstrate that KHIM significantly outperforms various state-of-the-art approaches, highlighting its effectiveness in leveraging knowledge graph information for better recommendations.