I am a PhD student in UC Berkeley Vision Science, on computational vision track, advised by Prof. Stella Yu. Previously I was a research intern at Sensetime. I received my B.E in Electronic Engineering from XJTU in 2018, where I worked with Prof. Jinjun Wang at Institute of Artificial Intelligence and Robotics.

Spatial Transformer for 3D Point Clouds
Jiayun Wang, Rudrasis Chakraborty, Stella X. Yu.
arXiv 2019. [paper] [project] [code]

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

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

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.

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.

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]

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.

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.

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

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

Hierarchical Model for Long-term Video Prediction
Jiayun Wang, Zhongxia Yan, Jeffrey Zhang.
CS 280 (Computer Vision) Final Project. [demo] [report] [slides] [code]

We use a hierarchical approach for long-term video prediction, aiming at estimating high-level structure in the input frame first, then predicting how that structure grows in the future.

Perceptual Metric for Realistic DiffuserCam Imaging
Jiayun Wang, Linda F. Liu.
VS 260A (Optical and Neural Limits to Vision) Final Project. [slides]

We use a nerual network based perceptual metric for realistic DiffuserCam imaging.

Two-lens Camera Trap System
Jiayun Wang, Qiuyu Zhang, Jingwen Zhu, Zhongqi Miao.
CS 294-127 (Computational Imaging) Final Project. [paper] [slides]

We designed and implemented a stereo camera system which is able to estimate the height and moving direction of the animal.

Object Recognizing Surface Organizer
Loren Jiang, Jiayun Wang, Qiuyu Zhang and Jingwen Zhu.
EECS 106A (Robotics) Final Project. [website] [code: available on request]

Our robotic system (Baxter) is able to recognize a number unique objects on the workspace and move them to their desired positions with relatively low rates of failure.