The University of Queensland

Source-free progressive graph learning for open-set domain adaptation

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

Interpretable signed link prediction with signed infomax hyperbolic graph

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

Discovering collaborative signals for next POI recommendation with iterative Seq2Graph augmentation

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

Learning from the past: continual meta-learning with Bayesian Graph Neural Networks

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

Progressive graph learning for open-set domain adaptation

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 .

Hypercomplex context guided interaction modeling for scene graph generation

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. doi: 10.1016/j.patcog.2023.109634

Deep collaborative graph hashing for discriminative image retrieval

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

Source-free progressive graph learning for open-set domain adaptation

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

GSMFlow: generation shifts mitigating flow for generalized zero-shot learning

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

Interpretable signed link prediction with signed infomax hyperbolic graph

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

Collaborative learning for extremely low bit asymmetric hashing

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

High-order nonlocal Hashing for unsupervised cross-modal retrieval

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

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

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

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

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

Optimizing Taxi Route Planning Based on Taxi Trajectory Data Analysis

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

Learning efficient unsupervised satellite image-based building damage detection

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

Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error

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

Open-RoadAtlas: Leveraging VLMs for Road Condition Survey with Real-Time Mobile Auditing

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

How Far Pre-trained Models Are from Neural Collapse on the Target Dataset Informs their Transferability

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

KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection

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

Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling

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

Exploring active 3D object detection from a generalization perspective

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

FFM: injecting out-of-domain knowledge via factorized frequency modification

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

Point to rectangle matching for image text retrieval

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

FluMA: An Intelligent Platform for Influenza Monitoring and Analysis

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

Discovering domain disentanglement for generalized multi-source domain adaptation

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

Conditional Extreme Value Theory for Open Set Video Domain Adaptation

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

RoadAtlas: intelligent platform for automated road defect detection and asset management

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

Mitigating Generation Shifts for Generalized Zero-Shot Learning

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

Semantics disentangling for generalized zero-shot learning

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

Discovering collaborative signals for next POI recommendation with iterative Seq2Graph augmentation

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

Learning to diversify for single domain generalization

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

Adversarial bipartite graph learning for video domain adaptation

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

Prototype-matching graph network for heterogeneous domain adaptation

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

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

Learning from the past: continual meta-learning with Bayesian Graph Neural Networks

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

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

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

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

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

Progressive graph learning for open-set domain adaptation

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 .

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

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

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

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

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

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

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

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

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

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

Visual learning from imperfect data via inductive bias modelling

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