Yue Yao

I am currently a Research Officer at the School of Computing of the Australian National University, working with Prof. Tom Gedeon. Before this role, I completed my PhD at ANU, during which I was fortunate to get supervision from Prof. Tom Gedeon and Prof. Liang Zheng.

During my PhD, I interned at NVIDIA, working on Metropolis with Dr. Milind Naphade and Dr. Thomas Tang.

Email  /  Google Scholar  /  Github

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  • Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond, TPAMI, Accepted. [PDF] [bibtex]


I'm interested in computer vision, deep learning, and brain computer interface. Much of my research is about optimizing the training set to improve model specificity on target domains, which is also my PhD topic. Selected publications are listed below.

prl Alice Benchmarks: Connecting Real World Object Re-Identification with the Synthetic
Xiaoxiao Sun, Yue Yao, Shengjin Wang, Hongdong Li, Liang Zheng
ICLR, 2024  

Within the Alice benchmarks, two object re-ID tasks are offered: person and vehicle re-ID. We collected and annotated two challenging real-world target datasets: AlicePerson and AliceVehicle for Syn2real re-ID research.

prl Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond
Yue Yao, Liang Zheng, Xiaodong Yang, Milind Napthade, Tom Gedeon
TPAMI, 2024  
PDF / Code

We aim to use graphic engines to simulate a large number of training data that have free annotations and possibly strongly resemble to real-world data.

prl Open-Set Facial Expression Recognition
Yuhang Zhang, Yue Yao, Xuannan Liu, Lixiong Qin, Wenjing Wang, Weihong Deng
AAAI, 2024  

We propose the open-set facial expression recognition task, and an strong baseline model for this task.

prl Optimizing the Training Set to Improve Model Specificity on Target Domains
Yue Yao
PhD Thesis, 2023  
prl Training with Product Digital Twins for AutoRetail Checkout
Yue Yao*, Xinyu Tian*, Zheng Tang, Sujit Biswas, Huan Lei, Tom Gedeon, Liang Zheng (* denotes equal contribution)
Arxiv, 2023  
PDF / Code

We aim to address the challenge in the absence of a labeled real-world training set for AutoRetail Checkout. We address this by data rendering from the given 3D retail models.

prl Large-scale Training Data Search for Object Re-identification
Yue Yao, Huan Lei, Tom Gedeon, Liang Zheng
CVPR, 2023  
PDF / Code

We present a search and pruning (SnP) solution to the training data search problem in object re-ID. It results in a training set 80% smaller than the source pool while achieving a similar or even higher re-ID accuracy.

prl Information-preserving Feature Filter for Short-term EEG Signals
Yue Yao, Josephine Plested, Tom Gedeon
Neurocomputing, 2020  
Paper / Code

We studied the feature fusion problem in EEG, and proposed a feature filter to filter out unwanted (e.g., privacy-related) features from EEG data.

prl Simulating Content Consistent Vehicle Datasets with Attribute Descent
Yue Yao, Liang Zheng, Xiaodong Yang, Milind Napthade, Tom Gedeon
ECCV, 2020  
Paper / Code / Demo / Video

We used content-adapted synthetic vehicle data to augment real vehicle re-ID data and get improved accuracy.

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