I am a PhD candidate in UC Berkeley Vision Science program, on computational vision track, advised by Prof. Stella Yu. My research interests include 3D vision, medical AI and efficient learning (efficient networks, learning from long-tailed data, etc.) 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.
3D and Scene Understanding
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.
Efficient Learning and Networks
Orthogonal convolutional neural networks is a light-cost regularizer which reduces the feature redundancy and improves network performance and robustness under attack.
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.
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.
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.