Yadan Luo

I am a Lecturer / Assistant Professor and ARC Discovery Early Career Researcher Awardee at the School of Electrical Engineering and Computer Science, The University of Queensland (UQ), Australia. My research interests center on data-centric learning, aiming to leverage imperfect and inexact data to enhance model recognition in 2D/3D in open-world environments. I was honored with the Google PhD Fellowship and ICT Young Achiever, Women in Technology (WiT). I earned my PhD in Computer Science from UQ in 2021, under the supervision of Prof Helen Huang and Dr Mahsa Baktahmotlagh. I completed my bachelor’s degree in Computer Science from Yingcai Honors College, University of Electronic Science and Technology of China (UESTC) in 2017.

I am recruiting PhDs in computer vision and machine learning. Please feel free to drop me an email with your CV. Scholarships may be available for the 2025-July enrollment.

News

Nov, 2024	Grateful to receive both the Outstanding Service Award and the Outstanding Area Chair Award at ACM MM 2024. Thank you to the community for the recognition and support! 🌟
Oct, 2024	I will serve as an Area Chair for CVPR 2025.
Oct, 2024	I will serve as an Area Chair for ICLR 2025.
Oct, 2024	3 papers on prompt expansion for class-agnostic detection, dataset distillation and cross-modal retrieval with incomplete labels has been accepted by NeurIPS 2024!
Jul, 2024	Our survey on online test-time adaptation has been accepted by IJCV.
Jul, 2024	One paper on test-time adaptation for 3D detection has been accepted by MM 2024.
Jul, 2024	One paper on open-vocabulary 3D detection has been accepted by ECCV 2024.
Dec, 2023	One research paper on out-of-distribution (OOD) detection has been accepted by ICLR 2024.
Dec, 2023	I will serve as an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) from 2024.
Dec, 2023	I have been awarded the Early Career Research Award, EAIT, The University of Queensland.

Selected Publications

ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection

Bo Peng*, Yadan Luo*, Yonggang Zhang, Yixuan Li, and 1 more author

In International Conference on Learning Representations, ICLR 2024 2024

ICLR

arXiv Bib

@inproceedings{DBLP:conf/iclr/conjnorm,
  author = {Peng*, Bo and Luo*, Yadan and Zhang, Yonggang and Li, Yixuan and Fang, Zhen},
  title = {ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection},
  booktitle = {International Conference on Learning Representations, {ICLR} 2024},
  publisher = {OpenReview.net},
  year = {2024},
}

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

Yadan Luo, Zhuoxiao Chen, Zhen Fang, Zheng Zhang, and 2 more authors

In 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023

ICCV

arXiv Bib Code

@inproceedings{DBLP:conf/iccv/Luo23,
  author = {Luo, Yadan and Chen, Zhuoxiao and Fang, Zhen and and Zheng Zhang and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection},
  booktitle = {2023 {IEEE/CVF} International Conference on Computer Vision, {ICCV}},
  publisher = {{IEEE}},
  year = {2023},
}

Exploring Active 3D Object Detection from a Generalization Perspective

Yadan Luo, Zhuoxiao Chen, Zijian Wang, Xin Yu, and 2 more authors

In International Conference on Learning Representations, ICLR 2023 2023

ICLR

arXiv Bib Code

@inproceedings{DBLP:conf/iclr/crb,
  author = {Luo, Yadan and Chen, Zhuoxiao and Wang, Zijian and Yu, Xin and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {Exploring Active 3D Object Detection from a Generalization Perspective},
  booktitle = {International Conference on Learning Representations, {ICLR} 2023},
  publisher = {OpenReview.net},
  year = {2023},
}

Progressive Graph Learning for Open-Set Domain Adaptation

Yadan Luo, Zijian Wang, Zi Huang, and Mahsa Baktashmotlagh

In Proc. of the 37th International Conference on Machine Learning, ICML 2020 2020

ICML

Bib Code

@inproceedings{DBLP:conf/icml/LuoWHB20,
  author = {Luo, Yadan and Wang, Zijian and Huang, Zi and Baktashmotlagh, Mahsa},
  title = {Progressive Graph Learning for Open-Set Domain Adaptation},
  booktitle = {Proc. of the 37th International Conference on Machine Learning,
                 {ICML} 2020},
  series = {Proceedings of Machine Learning Research},
  volume = {119},
  pages = {6468--6478},
  publisher = {{PMLR}},
  year = {2020},
  timestamp = {Tue, 15 Dec 2020 00:00:00 +0100},
  biburl = {https://dblp.org/rec/conf/icml/LuoWHB20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
}