Overview
Background
Yadan Luo is currently a Senior Lecturer with Data Science Discipline, School of EECS, The University of Queensland. She received her BSc degree from University of Electronic Science and Technology of China, and her PhD in Computer Science from School of ITEE, The University of Queensland in 2017 and 2021 respectively. Her research interests mainly include machine learning from imperfect data, by leveraging domain adaptation, domain generalization, few-/zero-shot learning and active learning to empower the applications in computer vision and multimedia data analysis areas. Her work of image analysis published at Pattern Recognition Journal in 2018 is placed in the top 1% of the academic field of Engineering and is recognised as a Highly Cited Paper by Web of Science. Yadan was awarded the Google PhD Fellowship 2020 as a recognition of her research in the machine learning area and her strong potential of influencing the future of technology. She was also a recipient of ICT Young Achiever Award, Women in Technology (WiT.org) 2018 and a few other research awards.
[For Prospective Students] I am continuously looking for highly-motivated Ph.D. students to work on machine learning & multimedia data analysis, specifically for addressing domain shifts and generalisation issues. Please send me your CV if interested.
Availability
- Dr Yadan Luo is:
- Available for supervision
- Media expert
Qualifications
- Bachelor of Computer Science, University of Electronic Science and Technology of China
- Doctor of Philosophy, The University of Queensland
Research interests
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Multimedia Data Analysis
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Machine Learning
Domain adaptation, domain generalization
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3D Lidar-based Object Detection
Works
Search Professor Yadan Luo’s works on UQ eSpace
2020
Conference Publication
Human consensus-oriented image captioning
Wang, Ziwei, Huang, Zi and Luo, Yadan (2020). Human consensus-oriented image captioning. Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan, 7-15 January 2021. Palo Alto, CA, United States: AAAI Press. doi: 10.24963/ijcai.2020/92
2020
Journal Article
Deep collaborative discrete hashing with semantic-invariant structure construction
Wang, Zijian, Zhang, Zheng, Luo, Yadan, Huang, Zi and Shen, Heng Tao (2020). Deep collaborative discrete hashing with semantic-invariant structure construction. IEEE Transactions on Multimedia, 23 9096547, 1274-1286. doi: 10.1109/tmm.2020.2995267
2020
Conference Publication
CANZSL: Cycle-consistent adversarial networks for zero-shot learning from natural language
Chen, Zhi, Li, Jingjing, Luo, Yadan, Huang, Zi and Yangyang, Yangyang (2020). CANZSL: Cycle-consistent adversarial networks for zero-shot learning from natural language. IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO United States, 1-5 March 2020. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACV45572.2020.9093610
2020
Conference Publication
Fashion recommendation with multi-relational representation learning
Li, Yang, Luo, Yadan and Huang, Zi (2020). Fashion recommendation with multi-relational representation learning. 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, Singapore, 11-14 May 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-47426-3_1
2020
Conference Publication
PAIC: Parallelised Attentive Image Captioning
Wang, Ziwei, Huang, Zi and Luo, Yadan (2020). PAIC: Parallelised Attentive Image Captioning. 31st Australasian Database Conference, ADC 2020, Melbourne, VIC, Australia, February 3–7, 2020. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-39469-1_2
2020
Journal Article
Inductive structure consistent hashing via flexible semantic calibration
Zhang, Zheng, Liu, Luyao, Luo, Yadan, Huang, Zi, Shen, Fumin, Shen, Heng Tao and Lu, Guangming (2020). Inductive structure consistent hashing via flexible semantic calibration. IEEE Transactions on Neural Networks and Learning Systems, 32 (10), 1-15. doi: 10.1109/tnnls.2020.3018790
2020
Conference Publication
Semi-supervised cross-modal hashing with graph convolutional networks
Duan, Jiasheng, Luo, Yadan, Wang, Ziwei and Huang, Zi (2020). Semi-supervised cross-modal hashing with graph convolutional networks. Australasian Database Conference, Melbourne, VIC, Australia, 3-7 February 2020. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-39469-1_8
2020
Conference Publication
Graph-based relation-aware representation learning for clothing matching
Li, Yang, Luo, Yadan and Huang, Zi (2020). Graph-based relation-aware representation learning for clothing matching. Australasian Database Conference, Melbourne, VIC, Australia, 3-7 February 2020. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-39469-1_15
2019
Journal Article
Deep reinforcement learning enabled self-learning control for energy efficient driving
Qi, Xuewei, Luo, Yadan, Wu, Guoyuan, Boriboonsomsin, Kanok and Barth, Matthew (2019). Deep reinforcement learning enabled self-learning control for energy efficient driving. Transportation Research Part C: Emerging Technologies, 99, 67-81. doi: 10.1016/j.trc.2018.12.018
2019
Conference Publication
Collaborative generative adversarial network for recommendation systems
Tong, Yuzhen, Luo, Yadan, Zhang, Zheng, Sadiq, Shazia and Cui, Peng (2019). Collaborative generative adversarial network for recommendation systems. 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), Macao, 8-12 April 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDEW.2019.00-16
2019
Conference Publication
Context-aware attention-based data augmentation for POI recommendation
Li, Yang, Luo, Yadan, Zhang, Zheng, Sadiq, Shazia and Cui, Peng (2019). Context-aware attention-based data augmentation for POI recommendation. 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), Macao, 8-12 April 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDEW.2019.00-14
2019
Conference Publication
Deep collaborative discrete hashing with semantic-invariant structure
Wang, Zijian, Luo, Yadan, Zhang, Zheng and Huang, Zi (2019). Deep collaborative discrete hashing with semantic-invariant structure. 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Paris, France, 21-25 July 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3331184.3331275
2019
Conference Publication
Curiosity-driven reinforcement learning for diverse visual paragraph generation
Luo, Yadan, Huang, Zi, Zhang, Zheng, Wang, Ziwei, Li, Jingjing and Yang, Yang (2019). Curiosity-driven reinforcement learning for diverse visual paragraph generation. 27th ACM International Conference on Multimedia (MM), Nice, France, 21-25 October 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3343031.3350961
2019
Conference Publication
Cycle-consistent diverse image synthesis from natural language
Chen, Zhi and Luo, Yadan (2019). Cycle-consistent diverse image synthesis from natural language. 2019 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Shanghai, China, 8-12 July 2019. Piscataway, NJ, United States: IEEE. doi: 10.1109/icmew.2019.00085
2018
Conference Publication
Coarse-to-fine annotation enrichment for semantic segmentation learning
Luo, Yadan, Wang, Ziwei, Huang, Zi, Yang, Yang and Zhao, Cong (2018). Coarse-to-fine annotation enrichment for semantic segmentation learning. 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22-26 October 2018. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3269206.3271672
2018
Conference Publication
Look deeper see richer: Depth-aware image paragraph captioning
Wang, Ziwei, Luo, Yadan, Li, Yang, Huang, Zi and Yin, Hongzhi (2018). Look deeper see richer: Depth-aware image paragraph captioning. 26th ACM Multimedia conference, MM 2018, Seoul, South Korea, October 22 - 26, 2018. New York, NY, Untied States: Association for Computing Machinery, Inc. doi: 10.1145/3240508.3240583
2017
Journal Article
Robust discrete code modeling for supervised hashing
Luo, Yadan, Yang, Yang, Shen, Fumin, Huang, Zi, Zhou, Pan and Shen, Heng Tao (2017). Robust discrete code modeling for supervised hashing. Pattern Recognition, 75, 128-135. doi: 10.1016/j.patcog.2017.02.034
2017
Conference Publication
Deep reinforcement learning-based vehicle energy efficiency autonomous learning system
Qi, Xuewei, Luo, Yadan, Wu, Guoyuan, Boriboonsomsin, Kanok and Barth, Matthew J. (2017). Deep reinforcement learning-based vehicle energy efficiency autonomous learning system. IEEE Intelligent Vehicles Symposium, Redondo Beach, CA, United States, 11-14 June 2017. Piscataway, NJ, United States: IEEE. doi: 10.1109/ivs.2017.7995880
2016
Conference Publication
Zero-shot hashing via transferring supervised knowledge
Yang, Yang, Luo, Yadan, Chen, Weilun, Shen, Fumin, Shao, Jie and Shen, Heng Tao (2016). Zero-shot hashing via transferring supervised knowledge. 24th ACM Multimedia Conference, MM 2016, Amsterdam, The Netherlands, 15 - 19 October 2016. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2964284.2964319
Funding
Current funding
Past funding
Supervision
Availability
- Dr Yadan Luo is:
- Available for supervision
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Supervision history
Current supervision
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Doctor Philosophy
Maximizing Object Detection Dataset Efficiency through the Power of Active Learning
Principal Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Unsupervised Domain Adaptation on 3D Object Detection and Segmentation
Principal Advisor
Other advisors: Professor Helen Huang, Associate Professor Mahsa Baktashmotlagh
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Doctor Philosophy
Information Dissemination Prediction for Emerging Video Sharing Platforms
Principal Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Data-Centric Advancements in Neural Radiance Fields for 3D Reconstruction
Principal Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Multimodal Sensing System for 3D Vision
Principal Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Towards Open-vocabulary 3D Object Detection
Principal Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Developing Efficient and Stable Perovskite Quantum Dot Light Emitting Diodes
Associate Advisor
Other advisors: Professor Lianzhou Wang
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Doctor Philosophy
Multimodal Representation Learning for Responsive Video-sharing Services
Associate Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Enhancing Safety and Reliability of machine learning models using lifelong multi-modal learning
Associate Advisor
Other advisors: Professor Helen Huang, Associate Professor Mahsa Baktashmotlagh
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Doctor Philosophy
Enhancing Plant Phenotyping Accuracy through Analysing Video Data
Associate Advisor
Other advisors: Professor Scott Chapman, Associate Professor Mahsa Baktashmotlagh
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Doctor Philosophy
Developing Efficient and Stable Perovskite Quantum Dot Light Emitting Diodes
Associate Advisor
Other advisors: Professor Lianzhou Wang
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Doctor Philosophy
Active Learning for Multi-modal 3D Perception
Associate Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Towards Explainable Multi-source Multivariate Time-series Analysis
Associate Advisor
Other advisors: Professor Helen Huang, Associate Professor Sen Wang
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Doctor Philosophy
Towards Practical Adaptation to Domain Shift in Deep Vision Learning
Associate Advisor
Other advisors: Professor Helen Huang, Associate Professor Sen Wang
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Doctor Philosophy
Developing efficient and stable perovskite quantum dot light emitting diodes
Associate Advisor
Other advisors: Professor Lianzhou Wang
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Doctor Philosophy
Developing efficient and stable perovskite quantum dot light emitting diodes
Associate Advisor
Other advisors: Professor Lianzhou Wang
Completed supervision
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2023
Doctor Philosophy
Monocular 3D Reconstruction: Shape Representation, Scalability and Generalization.
Associate Advisor
Other advisors: Associate Professor Mahsa Baktashmotlagh
Media
Enquiries
Contact Dr Yadan Luo directly for media enquiries about their areas of expertise.
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