I am a PhD candidate at UC Berkeley, advised by Prof. Stella Yu and Prof. Meng Lin. My research lies at the intersection of machine learning, computer vision and applications to medicine. I received my B.E in Electronic Engineering from Xi'an Jiaotong University in 2018, where I worked with Prof. Jinjun Wang at the Institute of Artificial Intelligence and Robotics.

News! Recurrent Parameter Generators is accepted to WACV 2023!
News! I am on schedule to graduate in May 2023. I can work in the US without sponsorship.

I develop computer vision and machine learning methods that learn structures from natural data, where I make learning mechanisms, models and learned representations all be aware of the structures of the data. Such methods could unleash new applications in medicine and active vision. To that end, my research focuses on: 1) Developing self-supervised and long-tailed mechanisms for learning structures from natural data. 2) Improving training and inference efficiency with structure-aware models. 3) Learning geometry-aware representations for understanding the environment and making actions. See my Research Statement for more details.

Unsupervised Scene Sketch to Photo Synthesis
Jiayun Wang, Sangryul Jeon, Stella X. Yu, Xi Zhang, Himanshu Arora, Yu Lou.
ECCVW 2022. [project] [paper] [code] [media coverage]



Spatial Transformer for 3D Point Clouds
Jiayun Wang, Rudrasis Chakraborty, Stella X. Yu.
IEEE TPAMI 2021 [paper] [project] [code]

We propose to learn different non-rigid transformations of the input point cloud for different local neighborhoods at each layer.

Orthogonal Convolutional Neural Networks
Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, Stella X. Yu.
CVPR 2020. [paper] [project] [code] [blog (Chinese)]

Orthogonal convolutional neural networks is a light-cost regularizer which reduces the feature redundancy and improves network performance and robustness under attack.

Sur-Real: Frechet Mean and Distance Transform for Complex-Valued Deep Learning
Rudrasis Chakraborty, Jiayun Wang, Stella X. Yu.
CVPRW 2019. Best Paper [paper] [poster] [code]

This work develops a novel deep learning architecture for naturally complex-valued data, with improved results and only 10% of the parameters as the baseline model.

Large-scale Long-Tailed Recognition in an Open World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu.
CVPR 2019 Oral. [paper] [project] [code] [blog]

This work formally defines the problem of open long-tailed recognition (OLTR) as optimizing for the overall accuracy of a naturally-distributed dataset with the presence of open classes.

Insights and Approaches Using Deep Learning to Classify Wildlife
Zhongqi Miao, Kaitlyn M Gaynor, Jiayun Wang, Ziwei Liu, Oliver Muellerklein, Mohammad S. Norouzzadeh, Alex McInturff, Rauri C. K. Bowie, Ran Nathon, Stella X. Yu, Wayne M. Getz.
Nature - Scientific Reports 2019. [Paper]

This work aims to interpret the concepts behind the convolutional neural networks (CNNs) in classifying wildlife.

Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification
Jiayun Wang, Sanping Zhou, Jinjun Wang, Qiqi Hou.
Pattern Recognition 2018. [paper] [arxiv] [code]

This work designs a dynamically adaptive loss function to overcome the drawbacks of conventional loss functions for person re-identification.

Successive Embedding and Classification Loss for Aerial Image Classification
Jiayun Wang, Patrick Virtue, Stella X. Yu.
arXiv 2017. [paper] [code]

This work aims to address the overfitting problem in remote sensing image classification.

Point to Set Similarity Based Deep Feature Learning for Person Re-identification
Sanping Zhou, Jinjun Wang, Jiayun Wang, Yihong Gong, Nanning Zheng.
CVPR 2017. [paper] [code]

This work presents a novel metric learning method based on P2S similarity comparison for person re-identification.

Predicting demographics from meibography using deep learning
Jiayun Wang, Andrew D. Graham, Stella X. Yu, Meng C. Lin.
Nature - Scientific Reports 2022. [paper]



A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images
Jiayun Wang, Thao N. Yeh, Rudrasis Chakraborty, Stella X. Yu, Meng C. Lin.
Translational Vision Science and Technology 2019. [paper] [code]

This work develops a deep learning approach to digitally segmenting meibomian gland atrophy area and computing percent atrophy in meibography images.

At Berkeley, I taught the following classes as a graduate student instructor:
      CS189/289, Machine Learning, Fall 2020
      VS205, Visual Perception, Fall 2019, 2018