Chao Feng

I am a second-year master's student at the University of Michigan (UMich) majoring in Electrical and Computer Engineering (ECE) of EECS Department, advised by Prof. Andrew Owens.

I got my Bachelor's degree in Electronic Information Engineering from University of Electronic Science and Technology of China (UESTC), where I was advised by Prof. Shuaicheng Liu.

Email: chfeng at umich dot edu

I am currently applying for Ph.D. positions for Fall 2023!

Email  /  CV  /  Google Scholar  /  Github

profile photo

I'm interested in computer vision, self-supervised learning, representation learning, audio-visual learning, multimodal learning, and image forensics. I am also open to other areas!

b3do Self-Supervised Video Forensics by Audio-Visual Anomaly Detection
Chao Feng, Ziyang Chen, Andrew Owens,
Under Review
project page / arXiv / code

We learn several feature sets in a self-supervised manner by using audio-visual synchronization task and utilize autoregressive model to do anomaly detection on top of each feature set for video forensics detection.

b3do ACML: Augmented Competitive Metric Learning for Image Ordinal Classification
Chao Zhang*, Chao Feng*, Jianmei Cheng, Shuaicheng Liu, Ce Zhu,
Under Review
project page / arXiv / code

We design a novel loss to embed ordinal information into training procedure and use augmented way to learn discriminative representation for image ordinal classification (IOC) task .

b3do AVA-AVD: Audio-Visual Speaker Diarization in the Wild
Eric Zhongcong Xu, Zeyang Song, Satoshi Tsutsui, Chao Feng, Mang Ye, Mike Zheng Shou,
ACM Multimedia, 2022
project page / arXiv / code

We create the AVA Audio-Visual Diarization (AVA-AVD) dataset to develop diarization methods for in-the-wild videos.

Side Projects
b3do NeRF implementation

b3do Image Super-Resolution
EECS 598 project

b3do MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy Severity Prediction
EECS 545 final project

University of Michigan, M.S. in Electrical and Computer Engineering
Academic performance and related courses GPA: 4.0/4.0
EECS 504: Foundations of Computer Vision (A) Instructor: Andrew Owens
EECS 545: Machine Learning (CSE) (A) Instructors: Honglak Lee and MichaƂ DereziƄski
EECS 598: Deep Learning for Computer Vision (A+) Instructor: Justin Johnson
EECS 551: Matrix Methods for Signal Processing, Data Analysis and Machine Learning (A) Instructor: Jeffrey Fessler
EECS 501: Probability and Random Processes (A) Instructor: Lei Ying
EECS 498: Principles of Machine Learning (A) Instructor: Qing Qu
University of Electronic Science and Technology of China, B.Eng. in Electronic Information Engineering
Academic performance GPA: 88.79/100 (3.86/4.0)