Richard Zhang

Research Scientist
Adobe Research
San Francisco, CA

rizhang at adobe.com
[GitHub] [Google Scholar] [Resume/CV]


My research interests are in computer vision, machine learning, deep learning, graphics, and image processing. I was previously a PhD student at UC Berkeley, advised by Prof. Alexei (Alyosha) Efros. I obtained BS and MEng degrees from Cornell University in ECE.


News

[Jun 2019] See our new work on detecting Photoshopped images below.
[May 2019] Our work on anti-aliasing convolutional networks has been accepted to ICML 2019. Try anti-aliasing your convnet here!


Publications

Making Convolutional Networks Shift-Invariant Again
Richard Zhang.
In ICML, 2019.
[Paper] [Webpage] [GitHub] [Talk] [Slides (127mb)] [Poster] [Bibtex]
Detecting Photoshopped Faces by Scripting Photoshop
Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, Alexei A. Efros
To appear in ICCV, 2019.
[Paper] [Webpage] [GitHub] [Video] [Adobe Blog] [Bibtex]
Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation
Arnab Ghosh, Richard Zhang, Puneet Dokania, Oliver Wang, Alexei A. Efros, Philip H.S. Torr, Eli Shechtman To appear in ICCV, 2019.
[Paper (coming soon)] [Bibtex]
Deep Parametric Shape Predictions using Distance Fields
Dmitriy Smirnov, Matthew Fisher, Vladimir G. Kim, Richard Zhang, Justin Solomon
In Arxiv, 2019.
[Paper] [Webpage] [Bibtex]
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
In CVPR, 2018.
[Paper] [Webpage] [GitHub] [Poster] [Adobe Blog] [Two Min Papers] [Bibtex]
Stochastic Adversarial Video Prediction
Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine
In ArXiv, 2018.
[Paper] [Webpage] [GitHub] [Bibtex]
Toward Multimodal Image-to-Image Translation
Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman
In NIPS, 2017.
[Paper] [Webpage] [GitHub] [Video (YouTube)(mp4)] [Poster] [Two Min Papers] [Bibtex]
Real-Time User-Guided Image Colorization with Learned Deep Priors
Richard Zhang*, Jun-Yan Zhu*, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros
(*indicates equal contribution)
In SIGGRAPH, 2017.
[Paper] [Webpage] [Fastforward] [Talk] [Video (YouTube)(mp4)] [GitHub] [Slides (141mb)] [Bibtex]
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
Richard Zhang, Phillip Isola, Alexei A. Efros
In CVPR, 2017.
[Paper] [Webpage] [GitHub] [Poster] [Seminar Talk] [Bibtex]
Colorful Image Colorization
Richard Zhang, Phillip Isola, Alexei A. Efros
In ECCV, 2016 (oral presentation).
[Paper] [Webpage] [GitHub] [Talk] [Slides (138mb)] [Poster] [Bibtex]
Sensor Fusion for Semantic Segmentation of Urban Scenes
Richard Zhang, Stefan Candra, Kai Vetter, Avideh Zakhor
In ICRA, 2015.
[Paper (pdf)(official)] [Slides] [Poster] [Talk] [Annotations (tar)(zip) ] [Bibtex]
Automatic Identification of Window Regions on Indoor Point Clouds Using LiDAR and Cameras
Richard Zhang, Avideh Zakhor
In WACV, 2014.
[Paper (pdf)(official)] [Bibtex]

Thesis

Image Synthesis for Self-Supervised Visual Representation Learning
Richard Zhang
Spring 2018.
[Thesis] [Dissertation Talk] [Fast Forward] [Slides (396 MB)] [Bibtex]

Awards

Reviewer recognitions, CVPR 2018, NeurIPS 2019
Thesis Fast Forward, Best Presentation, SIGGRAPH 2018
Adobe Research Fellowship 2017

Student collaborators/interns

I have had the opportunity to work with some wonderful collaborators.

Interns [Doctoral]
Taesung Park, UC Berkeley
Arnab Ghosh, Oxford
Minyoung (Jacob) Huh, MIT
Tamar Rott Shaham, Technion
Peiye Zhuang, UIUC
Dima Smirnov, MIT
Noa Fish, Tel Aviv

Interns [Masters]
Seungjoo Yoo, Korea Univ (Adobe WIT scholarship winner)

University [Doctoral]
Pranay Manocha, Princeton
Rawan Alghofaili, George Mason
Alvin Wan, UC Berkeley

University [Undergraduate]
Sheng-Yu Wang, UC Berkeley

Undergraduates (when I was at UC Berkeley)
Xin Qin, now @ USC
Hemang Jangle
Angela S. Lin, now @ UT Austin
Xinyang Geng, now @ UC Berkeley
Tianhe Yu, now @ Stanford
Stefan A. Candra

Internship

If you have similar interests and are interested in collaborating during a summer 2020 internship, please feel free to contact me. Timing-wise, I will be at ICCV (last week of Oct) and am available to meet. Otherwise, unless you have an impending offer, please contact me after Nov 15 (the CVPR deadline). Tell me about your past research experience and what you would potentially like to do. The goal of an internship is a publication, usually CVPR or SIGGRAPH. For example, see my NIPS 2017 and CVPR 2018 papers, which were from my summer 2017 internship. Interns are typically PhD students. The number of slots is limited, so we unfortunately cannot accept everyone.

Teaching

Introduction to Artificial Intelligence (CS 188), UC Berkeley
Graduate Student Instructor (GSI) with Prof. Anca Dragan
Spring 2017

Computer Vision (CS 280), UC Berkeley
Graduate Student Instructor (GSI) with Prof. Alexei A. Efros, Prof. Trevor Darrell
Spring 2016

Introduction to Circuits (ECE 2100), Cornell University
Teaching Assistant (TA) with Prof. Alyosha Molnar
Spring 2010