Jun Zhou

Professor

Griffith University

Australia

Research Interests

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About

Jun Zhou is a Professor at Griffith University in Australia. His research areas include image processing and analysis, particularly in hyperspectral imaging, semantic segmentation, and machine learning applications for remote sensing. Recent publications demonstrate his work on efficient knowledge distillation, spatial-spectral transformers for image denoising, and advanced techniques for detecting agricultural damage and enhancing UAV-based object detection.

Articles (29)

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2026

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2025

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2022

Collaborators (5)

Xinwen Liu

University of Queensland

AUSTRALIA

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Ugochukwu Akpudo

Griffith University

AUSTRALIA

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Gervase Tuxworth

Lecturer

Griffith University

AUSTRALIA

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Edwin Hancock

Emeritus Professor

University of York

UNITED KINGDOM

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Alan Wee-Chung Liew

Griffith University

AUSTRALIA

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Jun Zhou

Research Interests

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About

Articles (29)

SATE: Efficient knowledge distillation with implicit student-aware teacher ensembles

CoPISan: Contrastive Perceptual Inference and Sanity Checks for Concept-Based CNN Explanations

SSIT: A spatial–spectral interactive transformer for hyperspectral image denoising

Single-wavelength near-infrared imaging and machine learning for detecting Queensland fruit fly damage in cherries

Hierarchical Context Learning of object components for unsupervised semantic segmentation

IPT-ILR: Image Pyramid Transformer Coupled With Information Loss Regularization for All-in-One Image Restoration

A Robust Co-Training Framework to Handle Noisy Labels for Remote Sensing Image Segmentation

Adversarial Dilated Large-Kernel Attention Networks for Cross-Domain Few-Shot Hyperspectral Image Classification

Boost UAV-Based Object Detection via Scale-Invariant Feature Disentanglement and Adversarial Learning

HSLiNets: Evaluating Band Ordering Strategies in Hyperspectral and LiDAR Fusion

Few-shot classification guided by generalization error bound

Early detection of sugarcane smut and mosaic diseases via hyperspectral imaging and spectral-spatial attention deep neural networks

Diffusion-Model-Based Hyperspectral Unmixing Using Spectral Prior Distribution

Few-shot Classification Model Compression via School Learning

Hy-Tracker: A Novel Framework for Enhancing Efficiency and Accuracy of Object Tracking in Hyperspectral Videos

Hyperspectral Image Denoising via Spatial–Spectral Recurrent Transformer

Iterative Low-Rank Network for Hyperspectral Image Denoising

Learning From Human Attention for Attribute-Assisted Visual Recognition

LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification

Mask-Guided Local–Global Attentive Network for Change Detection in Remote Sensing Images

Attribute subspaces for zero-shot learning

Learning consistent region features for lifelong person re-identification

Learning a Deep Ensemble Network With Band Importance for Hyperspectral Object Tracking

Multitask Sparse Representation Model-Inspired Network for Hyperspectral Image Denoising

Self-Supervised Spectral–Spatial Graph Prototypical Network for Few-Shot Hyperspectral Image Classification

SiamBAG: Band Attention Grouping-Based Siamese Object Tracking Network for Hyperspectral Videos

Spatial–Spectral Split Attention Residual Network for Hyperspectral Image Classification

Toward Universal Representation Learning for Multidomain Hyperspectral Image Classification

A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

Collaborators (5)

Xinwen Liu

Ugochukwu Akpudo

Gervase Tuxworth

Edwin Hancock

Alan Wee-Chung Liew

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