All Outputs (19)

Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network (2024)
Journal Article
Xie, Y., Jin, L., Zhu, C., Luo, W., & Wang, Q. (2024). Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network. Engineering Applications of Artificial Intelligence, 139(Part B), Article 109668. https://doi.org/10.1016/j.engappai.2024.109668

Recent advancements in Artificial Intelligence (AI), particularly deep learning, have significantly improved lithology identification in reservoir exploration by leveraging micrographic rock imagery. Deep neural networks excel in feature extraction,... Read More about Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network.

Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation (2024)
Journal Article
Zhu, C., Zhang, L., Luo, W., Jiang, G., & Wang, Q. (2025). Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation. Neural Networks, 181, Article 106859. https://doi.org/10.1016/j.neunet.2024.106859

Unsupervised Domain Adaptation (UDA) is a machine learning technique that facilitates knowledge transfer from a labeled source domain to an unlabeled target domain, addressing distributional discrepancies between these domains. Existing UDA methods o... Read More about Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation.

SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding (2024)
Journal Article
Qiu, K., Zhang, Y., Ren, Z., Li, M., Wang, Q., Feng, Y., & Chen, F. (2024). SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding. Insects, 15(9), Article 667. https://doi.org/10.3390/insects15090667

Simple Summary: Cotton is a crucial economic crop, but it is often threatened by various pests and diseases during its growth, significantly impacting its yield and quality. Earlier image classification methods often suffer from low accuracy and stru... Read More about SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding.

Dynamic adversarial adaptation network with selective pseudo-labels for enhanced Unsupervised Domain Adaptation in rock microscopic image analysis (2024)
Journal Article
Xie, Y., Jin, L., Zhu, C., Luo, W., & Wang, Q. (2024). Dynamic adversarial adaptation network with selective pseudo-labels for enhanced Unsupervised Domain Adaptation in rock microscopic image analysis. Geoenergy Science and Engineering, 240, Article 213011. https://doi.org/10.1016/j.geoen.2024.213011

The critical role of lithology classification in reservoir exploration is increasingly germinating interest in intelligent rock image classification applications. Nonetheless, the efficacy of these classification methods predominan... Read More about Dynamic adversarial adaptation network with selective pseudo-labels for enhanced Unsupervised Domain Adaptation in rock microscopic image analysis.

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds (2024)
Journal Article
Ding, Y., Wang, M., Fu, Y., & Wang, Q. (2024). Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds. Forests, 15(5), Article 839. https://doi.org/10.3390/f15050839

Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overl... Read More about Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds.

A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism (2024)
Journal Article
Qiu, K., Zhang, Y., Zhao, J., Zhang, S., Wang, Q., & Chen, F. (2024). A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism. Electronics, 13(10), Article 1922. https://doi.org/10.3390/electronics13101922

The objective of multimodal sentiment analysis is to extract and integrate feature information from text, image, and audio data accurately, in order to identify the emotional state of the speaker. While multimodal fusion schemes have made some progre... Read More about A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism.

Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation (2024)
Journal Article
Wang, Q., Meng, F., & Breckon, T. P. (2024). Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation. IEEE Transactions on Artificial Intelligence, https://doi.org/10.1109/TAI.2024.3379940

Domain adaptation solves image classification problems in the target domain by taking advantage of the labelled source data and unlabelled target data. Usually, the source and target domains share the same set of classes. As a special case, Open-Set... Read More about Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation.

Multiview latent space learning with progressively fine-tuned deep features for unsupervised domain adaptation (2024)
Journal Article
Zhu, C., Wang, Q., Xie, Y., & Xu, S. (2024). Multiview latent space learning with progressively fine-tuned deep features for unsupervised domain adaptation. Information Sciences, 662, Article 120223. https://doi.org/10.1016/j.ins.2024.120223

Unsupervised Domain Adaptation (UDA) and Multi-source Domain Adaptation (MDA) have emerged as practical techniques to address the domain shift between source and target domains with different statistical distributions, where the target domain often h... Read More about Multiview latent space learning with progressively fine-tuned deep features for unsupervised domain adaptation.

Genome-Wide Identification of the ABC Gene Family and Its Expression in Response to the Wood Degradation of Poplar in Trametes gibbosa (2024)
Journal Article
Zhao, J., Wang, A., & Wang, Q. (2024). Genome-Wide Identification of the ABC Gene Family and Its Expression in Response to the Wood Degradation of Poplar in Trametes gibbosa. Journal of Fungi, 10(2), Article 96. https://doi.org/10.3390/jof10020096

Wood-rotting fungi’s degradation of wood not only facilitates the eco-friendly treatment of organic materials, decreasing environmental pollution, but also supplies crucial components for producing biomass energy, thereby reducing dependence on fossi... Read More about Genome-Wide Identification of the ABC Gene Family and Its Expression in Response to the Wood Degradation of Poplar in Trametes gibbosa.

DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring (2023)
Journal Article
Dai, S., Meng, F., Wang, Q., & Chen, X. (2024). DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring. Renewable and Sustainable Energy Reviews, 191, Article 114091. https://doi.org/10.1016/j.rser.2023.114091

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household level into appliance-level consumption, can help analyze the electricity consumption beha... Read More about DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring.

On Fine-tuned Deep Features for Unsupervised Domain Adaptation (2023)
Presentation / Conference Contribution
Wang, Q., Meng, F., & Breckon, T. (2023, June). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. Presented at IJCNN 2023: International Joint Conference on Neural Networks, Queensland, Australia

Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the specific source or target domain data for a particular domain adaptati... Read More about On Fine-tuned Deep Features for Unsupervised Domain Adaptation.

Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders (2023)
Journal Article
Wang, Q., & Breckon, T. (2023). Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders. Neural Networks, 163, 40-52. https://doi.org/10.1016/j.neunet.2023.03.033

Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classific... Read More about Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders.

Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation (2023)
Journal Article
Wang, Q., Meng, F., & Breckon, T. (2023). Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation. Neural Networks, 161, 614-625. https://doi.org/10.1016/j.neunet.2023.02.006

We address the Unsupervised Domain Adaptation (UDA) problem in image classification from a new perspective. In contrast to most existing works which either align the data distributions or learn domain-invariant features, we directly learn a unified c... Read More about Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation.

Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation (2021)
Journal Article
Wang, Q., & Breckon, T. (2022). Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation. Pattern Recognition, 123, Article 108362. https://doi.org/10.1016/j.patcog.2021.108362

Heterogeneous Domain Adaptation (HDA) addresses the transfer learning problems where data from the source and target domains are of different modalities (e.g., texts and images) or feature dimensions (e.g., features extracted with different methods).... Read More about Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation.

Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties (2021)
Journal Article
You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022). Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties. Applied Energy, 305, Article 117899. https://doi.org/10.1016/j.apenergy.2021.117899

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT’s predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency and cost saving.energy efficiency,... Read More about Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties.

Source Class Selection with Label Propagation for Partial Domain Adaptation (2021)
Presentation / Conference Contribution
Wang, Q., & Breckon, T. (2021, September). Source Class Selection with Label Propagation for Partial Domain Adaptation. Presented at International Conference on Image Processing, Anchorage, AK

In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes a... Read More about Source Class Selection with Label Propagation for Partial Domain Adaptation.

Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2021)
Presentation / Conference Contribution
Wang, Q., Bhowmik, N., & Breckon, T. (2020, December). Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. Presented at 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020), Miami, Florida

Automatic detection of prohibited objects within passenger baggage is important for aviation security. X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibite... Read More about Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery.

A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks (2019)
Presentation / Conference Contribution
Wang, Q., Ning, J., & Breckon, T. (2019, September). A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks. Presented at 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan

Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the b... Read More about A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks.

Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition (2019)
Presentation / Conference Contribution
Wang, Q., Bu, P., & Breckon, T. (2019, May). Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition. Presented at Proc. International Joint Conference on Neural Networks, Budapest

Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain. One limitation of the problem setting is th... Read More about Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition.