Dr Yadan Luo
School of Information Technology and Electrical Engineering
Lecturer
School of Electrical Engineering and Computer Science
Researcher biography
Yadan Luo is currently a Lecturer with Data Science Discipline, School of ITEE, 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.
Journal Articles
Wang, Zheng, Xu, Xing, Luo, Yadan, Wang, Guoqing and Yang, Yang (2023). Hypercomplex context guided interaction modeling for scene graph generation. Pattern Recognition, 141 109634, 109634. doi: 10.1016/j.patcog.2023.109634
Zhang, Zheng, Wang, Jianning, Zhu, Lei, Luo, Yadan and Lu, Guangming (2023). Deep collaborative graph hashing for discriminative image retrieval. Pattern Recognition, 139 109462, 1-14. doi: 10.1016/j.patcog.2023.109462
Luo, Yadan, Wang, Zijian, Chen, Zhuoxiao, Huang, Zi and Baktashmotlagh, Mahsa (2023). Source-free progressive graph learning for open-set domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 (9), 1-16. doi: 10.1109/tpami.2023.3270288
Chen, Zhi, Luo, Yadan, Wang, Sen, Li, Jingjing and Huang, Zi (2023). GSMFlow: generation shifts mitigating flow for generalized zero-shot learning. IEEE Transactions on Multimedia, 25 (99), 5374-5385. doi: 10.1109/tmm.2022.3190678
Luo, Yadan, Huang, Zi, Chen, Hongxu, Yang, Yang, Yin, Hongzhi and Baktashmotlagh, Mahsa (2021). Interpretable signed link prediction with signed infomax hyperbolic graph. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-1. doi: 10.1109/TKDE.2021.3139035
Luo, Yadan, Huang, Zi, Li, Yang, Shen, Fumin, Yang, Yang and Cui, Peng (2021). Collaborative learning for extremely low bit asymmetric hashing. IEEE Transactions on Knowledge and Data Engineering, 33 (12), 3675-3685. doi: 10.1109/tkde.2020.2977633
Zhang, Peng-Fei, Luo, Yadan, Huang, Zi, Xu, Xin-Shun and Song, Jingkuan (2021). High-order nonlocal Hashing for unsupervised cross-modal retrieval. World Wide Web, 24 (2), 563-583. doi: 10.1007/s11280-020-00859-y
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
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
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
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
Conference Papers
Yang, Xinyi, Chen, Zhi and Luo, Yadan (2024). Optimizing Taxi Route Planning Based on Taxi Trajectory Data Analysis. 34th Australasian Database Conference (ADC), Melbourne Australia, Nov 01-03, 2023. CHAM: SPRINGER INTERNATIONAL PUBLISHING AG. doi: 10.1007/978-3-031-47843-7_4
Zhang, Yiyun, Wang, Zijian, Luo, Yadan, Yu, Xin and Huang, Zi (2023). Learning efficient unsupervised satellite image-based building damage detection. 2023 IEEE International Conference on Data Mining (ICDM), Shanghai, China, 1-4 December 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/icdm58522.2023.00206
Etchegaray, Djamahl, Luo, Yadan, FitzChance, Zachary, Southon, Anthony and Zhong, Jinjiang (2023). Open-RoadAtlas: Leveraging VLMs for Road Condition Survey with Real-Time Mobile Auditing. New York, NY, USA: ACM. doi: 10.1145/3581783.3612668
Wang, Zixin, Luo, Yadan, Chen, Zhi, Wang, Sen and Huang, Zi (2023). Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error. New York, NY, USA: ACM. doi: 10.1145/3581783.3611808
Luo, Yadan, Chen, Zhuoxiao, Fang, Zhen, Zhang, Zheng, Baktashmotlagh, Mahsa and Huang, Zi (2023). KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection. IEEE/CVF International Conference on Computer Vision 2023 (ICCV), Paris, France, 2-6 October 2021. Paris, France: Computer Vision Foundation. doi: 10.1109/iccv51070.2023.01676
Chen, Zhuoxiao, Luo, Yadan, Wang, Zheng, Baktashmotlagh, Mahsa and Huang, Zi (2023). Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling. IEEE/CVF International Conference on Computer Vision 2023 (ICCV), Paris, France, 2-6 October 2023. Paris, France: Computer Vision Foundation. doi: 10.1109/iccv51070.2023.00344
Wang, Zijian, Luo, Yadan, Zheng, Liang, Huang, Zi and Baktashmotlagh, Mahsa (2023). How Far Pre-trained Models Are from Neural Collapse on the Target Dataset Informs their Transferability. IEEE/CVF International Conference on Computer Vision 2023 (ICCV), Paris, France, 2-6 October 2023. Paris, France: Computer Vision Foundation. doi: 10.1109/iccv51070.2023.00511
Luo, Yadan, Chen, Zhuoxiao, Wang, Zijian, Yu, Xin, Huang, Zi and Baktashmotlagh, Mahsa (2023). Exploring active 3D object detection from a generalization perspective. 11th International Conference on Learning Representations (ICLR), Kigali, Rwanda, 1 - 5 May 2023. New York, NY, United States: Cornell Tech. doi: 10.48550/arXiv.2301.09249
Wang, Zijian, Luo, Yadan, Huang, Zi and Baktashmotlagh, Mahsa (2023). FFM: injecting out-of-domain knowledge via factorized frequency modification. 23rd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, United States, 3-7 January 2023. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/wacv56688.2023.00412
Wang, Zheng, Gao, Zhenwei, Xu, Xing, Luo, Yadan, Yang, Yang and Shen, Heng Tao (2022). Point to rectangle matching for image text retrieval. 30th ACM International Conference on Multimedia, Lisbon, Portugal, 10-14 October 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3503161.3548237
Chen, Xi, Chen, Zhi, Wang, Zijian, Qiu, Ruihong and Luo, Yadan (2022). FluMA: An Intelligent Platform for Influenza Monitoring and Analysis. 33rd Australasian Database Conference (ADC), Sydney, NSW Australia, 2-4 September 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-15512-3_12
Wang, Zixin, Luo, Yadan, Zhang, Peng-Fei, Wang, Sen and Huang, Zi (2022). Discovering domain disentanglement for generalized multi-source domain adaptation. 2022 IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan, 18-22 July 2022. Piscataway, NJ United States: IEEE Computer Society. doi: 10.1109/icme52920.2022.9859733
Chen, Zhuoxiao, Zhang, Yiyun, Luo, Yadan, Wang, Zijian, Zhong, Jinjiang and Southon, Anthony (2021). RoadAtlas: intelligent platform for automated road defect detection and asset management. MMAsia '21: ACM Multimedia Asia, Gold Coast, QLD Australia, 1 - 3 December 2021. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3469877.3493589
Chen, Zhuoxiao, Luo, Yadan and Baktashmotlagh, Mahsa (2021). Conditional Extreme Value Theory for Open Set Video Domain Adaptation. MMAsia '21: ACM Multimedia Asia, Gold Coast, QLD Australia, 1 - 3 December 2021. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3469877.3490600
Chen, Zhi, Luo, Yadan, Wang, Sen, Qiu, Ruihong, Li, Jingjing and Huang, Zi (2021). Mitigating Generation Shifts for Generalized Zero-Shot Learning. MM '21: ACM Multimedia Conference, Online, 20 - 24 October 2021. Washington, DC United States: Association for Computing Machinery. doi: 10.1145/3474085.3475258
Chen, Zhi, Luo, Yadan, Qiu, Ruihong, Wang, Sen, Huang, Zi, Li, Jingjing and Zhang, Zheng (2021). Semantics disentangling for generalized zero-shot learning. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC Canada, 10-17 October 2021. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/iccv48922.2021.00859
Li, Yang, Chen, Tong, Luo, Yadan, Yin, Hongzhi and Huang, Zi (2021). Discovering collaborative signals for next POI recommendation with iterative Seq2Graph augmentation. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, QC Canada, 19 - 27 August 2021. Palo Alto, CA United States: A A A I Press. doi: 10.24963/ijcai.2021/206
Wang, Zijian, Luo, Yadan, Qiu, Ruihong, Huang, Zi and Baktashmotlagh, Mahsa (2021). Learning to diversify for single domain generalization. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC Canada, 10-17 October 2021. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICCV48922.2021.00087
Luo, Yadan, Huang, Zi, Wang, Zijian, Zhang, Zheng and Baktashmotlagh, Mahsa (2020). Adversarial bipartite graph learning for video domain adaptation. ACM International Conference on Multimedia, Seattle, WA, United States, 12-16 October 2020. New York, United States: Association for Computing Machinery. doi: 10.1145/3394171.3413897
Wang, Zijian, Luo, Yadan, Huang, Zi and Baktashmotlagh, Mahsa (2020). Prototype-matching graph network for heterogeneous domain adaptation. MM '20: 28th ACM International Conference on Multimedia, Online, October 2020. New York, NY, United States: ACM. doi: 10.1145/3394171.3413662
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
Luo, Yadan, Huang, Zi, Zhang, Zheng, Wang, Ziwei, Baktashmotlagh, Mahsa and Yang, Yang (2020). Learning from the past: continual meta-learning with Bayesian Graph Neural Networks. The Thirty-Fourth AAAI Conference on Artificial Intelligence/ The Thirty-Second Conference on Innovative Applications of Artificial Intelligence/ The Tenth Symposium on Educational Advances in Artificial Intelligence, New York, United States, 7-12 February 2020. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence (AAAI). doi: 10.1609/aaai.v34i04.5942
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
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
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
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
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
Luo, Yadan, Wang, Zijian, Huang, Zi and Baktashmotlagh, Mahsa (2020). Progressive graph learning for open-set domain adaptation. 37th International Conference on Machine Learning ICML 2020, Vienna, Austria, 12-18 July 2020 . International Machine Learning Society .
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
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
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
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
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
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
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
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
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
Thesis
Luo, Yadan (2021). Visual learning from imperfect data via inductive bias modelling. PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland. doi: 10.14264/bd5d3e6