Kaidi Cao

I am a first-year master student at Computer Science Department, Stanford University. I am working in the Stanford Vision and Learning Lab, under the supervision of Juan Carlos Niebles.

I graduated from Tsinghua University in July 2018. Prior to that, I have been lucky to work with Cheng Li(Sensetime) and Prof. Chen Change Loy (NTU) during my internship at Sensetime Research, as well as Dr. Jing Liao and Dr. Lu Yuan when I was a research intern at Visual Computing Group of Microsoft Research Lab - Asia.

My research interests lie primarily in the area of Computer Vision and Machine Learning. To be specific, I'm pariticularly intersted in Generative Adeversarial Networks and Video Analysis.

Email / LinkedIn / Google Scholar / Github

Education
stanford

Sept. 2018 - Jul. 2020 (Expected), Department of Computer Science, Stanford University ,

Master of Science, Computer Science.

tsinghua

Aug. 2014 - Jul. 2018 , Department of Electronic Engineering, Tsinghua University ,

Balchlor of Engineering, Electronic Information.

Publications
sigasia2018

CariGANs: Unpaired Photo-to-Caricature Translation
Kaidi Cao, Jing Liao, Lu Yuan
ACM Transactions on Graphics, (Proc. of Siggraph Asia), 2018
[pdf] [project page]

We present the first deep learning-based approach to automatically generate the facial caricature for a given portrait photo.

Press Coverage:
dream_mapping

Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
Kaidi Cao*, Yu Rong*, Cheng Li, Xiaoou Tang, Chen Change Loy
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
[pdf][project page][code]

We presented a Deep Residual EquivAriant Mapping (DREAM) block to improve the performance of face recognition on profile faces.

cluster_pipeline

Merge or Not? Learning to Group Faces via Imitation Learning
Yue He*, Kaidi Cao*, Cheng Li, Chen Change Loy
AAAI Conference on Artificial Intelligence (AAAI, Spotlight), 2018
[pdf][code]

We proposed a novel face grouping framework that makes sequential merging decision based on short- and long-term rewards via inverse reinforcement learning.

halide

DNN Dataflow Choice Is Overrated
Xuan Yang, Mingyu Gao, Jing Pu, Ankita Nayak, Qiaoyi Liu, Steven Emberton Bell, Jeff Ou Setter, Kaidi Cao, Heonjae Ha, Christos Kozyrakis, Mark Horowitz
Technical report, arXiv:1809.04070, 2018
[pdf]

We develop an auto-optimizer for DNN that is closely coupled with the Halide framework.

Academic Services
  • Reviewer for IJCV 2018

Honors and Awards
  • Academic Excellent Scholarship, Tsinghua, Univ, 2016, 2017

  • Award of Excellence for Internship Program, Microsoft Research Asia, 2018