About

I'm broadly interested in computer vision and machine learning. My research involves visual reasoning, vision and language, and image generation using deep neural networks.
I am currently a Research Scientist at Facebook AI Research.
I completed my PhD at Stanford University, advised by Fei-Fei Li. At Stanford I co-taught CS 231N: Convolutional Neural Networks for Visual Recognition with Fei-Fei Li, Andrej Karpathy (in 2016), and Serena Yeung (in 2017 and 2018).
Starting Fall 2019 I will join University of Michigan Computer Science and Engineering as an Assistant Professor. I am looking for strong students to join my research group, so please get in touch if you would like to work with me.

Publications

HiDDeN: Hiding Data With Deep Networks
Jiren Zhu*, Russell Kaplan*, Justin Johnson, Li Fei-Fei
[* indicates equal contribution]
ECCV 2018
Image Generation from Scene Graphs
Justin Johnson, Agrim Gupta, Li Fei-Fei
CVPR 2018
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, Alexandre Alahi
CVPR 2018
Inferring and Executing Programs for Visual Reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten,
Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick
ICCV 2017 (Oral)
Characterizing and Improving Stability in Neural Style Transfer
Agrim Gupta, Justin Johnson, Alexandre Alahi, Li Fei-Fei
ICCV 2017
CLEVR: A Diagnostic Dataset for
Compositional Language and Elementary Visual Reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten,
Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick
CVPR 2017
A Hierarchical Approach for Generating Descriptive Image Paragraphs
Jonathan Krause, Justin Johnson, Ranjay Krishna, Li Fei-Fei
CVPR 2017 (Spotlight)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Justin Johnson, Alexandre Alahi, Li Fei-Fei
ECCV 2016
Visual Genome: Connecting Language and Vision
using Crowdsourced Dense Image Annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson,
Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis,
Li-Jia Li, David A. Shamma, Michael S. Bernstein, Li Fei-Fei
IJCV 2016
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
Justin Johnson*, Andrej Karpathy*, Li Fei-Fei
[* indicates equal contribution]
CVPR 2016 (Oral)
Visualizing and Understanding Recurrent Networks
Andrej Karpathy*, Justin Johnson*, Li Fei-Fei
[* indicates equal contribution]
ICLR Workshop 2016
Love Thy Neighbors: Image Annotation by Exploiting Image Metadata
Justin Johnson*, Lamberto Ballan*, Li Fei-Fei
[* indicates equal contribution]
ICCV 2015
Image Retrieval using Scene Graphs
Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Ayman Shamma, Michael Bernstein, Li Fei-Fei
CVPR 2015

Side Projects

neural-style

A Torch implementation of the neural style transfer algorithm from the paper "A Neural Algorithm of Artistic Style" by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Inceptionism.

torch-rnn

Train character-level language models in torch, and sample from them to generate text. The language model is implemented with efficient, reusable RNN and LSTM modules.

simple-amt

A micro-framework that makes it easy to create and launch tasks on Amazon's Mechanical Turk.

cnn-benchmarks

Benchmarks for popular convolutional neural network models on different GPUs.