Richard Zhang

Research Scientist
Adobe Research
San Francisco, CA

rizhang at adobe.com
[GitHub] [Google Scholar]


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


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!
[Aug 2018] I will be presenting at the Thesis Fast Forward session at SIGGRAPH on Tuesday 8/14, 2:00pm.
[Jun 2018] We will be presenting our project on perceptual metrics at CVPR, Tuesday 6/19, 10:10am. Try our metric 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
In Arxiv, 2019.
[Paper] [Webpage] [GitHub] [Video] [Adobe Blog] [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]

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

Awards

Thesis Fast Forward, Best Presentation, SIGGRAPH 2018
Adobe Research Fellowship 2017


Collaborators

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

Graduate Researchers
Arnab Ghosh, from Oxford
Dima Smirnov, from MIT

Undergraduate Researchers
Sheng-Yu Wang
Hemang Jangle
Xin Qin (now @ USC)
Angela S. Lin (now @ UT Austin)
Xinyang Geng (now @ UC Berkeley)
Tianhe Yu (now @ Stanford)
Stefan A. Candra