Sanja Fidler
Curriculum Vitae
Work address:
Sanja Fidler
University of Toronto
6 King’s College Rd.
Room 386, Pratt Building
Toronto, Ontario M5S 3G4
Tel. +1 (416) 978-8737
Webpage: http://www.cs.toronto.edu/~fidler/
E-mail: fidler@cs.toronto.edu

Position
Associate Professor at University of Toronto
Vice President of AI Research at NVIDIA
Vector Institute (co-founder)

Research Interests
Computer Vision, Machine Learning, 3d deep learning, metaverse applications, multimodal representations, human-in-the-loop annotation

Degrees2010

Ph.D. in Computer Science
Department of Computer and Information Science, University of Ljubljana
Thesis title: Recognizing visual object categories with subspace methods and a learned
hierarchical shape vocabulary

2002

B.S. in Applied Mathematics
Department of Mathematics and Physics, University of Ljubljana
Thesis title: Independent Component Analysis

Employment
Jul 2020 –Associate Professor
University of Toronto

March 2022 –Vice President of AI Research
NVIDIA

July 2021 – Feb 2022

Sr. Director of AI
NVIDIAMay 2018 – June 2021 Director of AI
NVIDIA
Jul 2016 – Jun 2020

Assistant Professor (tenure-track)
University of Toronto

Jan 2014 – Jun 2016

Assistant Professor (non tenure-track)
University of Toronto

Jul 2012 – Jan 2014

Research Assistant Professor
Toyota Technological Institute at Chicago

Feb 2011 – Jun 2012

Postdoctoral Fellow
University of Toronto
Supervisor: Prof. Sven Dickinson

Jan – Aug 2010

Visiting Scientist
UC Berkeley and ICSISupervisor: Prof. Trevor Darrell

2008 – 2010

Research Assistant (Graduate)
Department of Computer and Information science, University of Ljubljana
Supervisor: Prof. Aleš Leonardis

2003 – 2007

Teaching Assistant (full time)
Department of Computer and Information Science, University of Ljubljana

2002

Research Assistant (Undergraduate)
Department of Electrical Engineering, University of Ljubljana
Project: Biomedical image analysis
Supervisor: Prof. Franjo Pernuš

Awards
2023

The AI 100 2023: The top people in artificial intelligence
The Business Insider

2023

Canadian Chair of AI (CCAI)
Awarded by CIFAR

2021

Innovation Award
Awarded by University of Toronto

2020

Connaught Innovation Award
Awarded by University of Toronto

2019

Early Researcher Award

2018

Canada CIFAR AI ChairAwarded by CIFAR

2018

Connaught New Researcher Award
Awarded by University of Toronto

2017

Best paper honorable mention at CVPR’17:
Annotating object instances with a polygon-RNN
Lluis Castrejon, Kaustav Kundu, Raquel Urtasun, Sanja Fidler

2016

Amazon Academic Research Award
Awarded by Amazon

2016

NVIDIA Pioneers of AI Award
Awarded by NVIDIA

2016

Facebook Faculty Award
Awarded by Facebook

2015

Teaching award
Awarded by CSSU at University of Toronto

2015

Best reviewer award
Computer Vision and Pattern Recognition (CVPR)

2012

Best reviewer award
Computer Vision and Pattern Recognition (CVPR)

2012

Best reviewer award
European Conference on Computer Vision (ECCV)2008

Best reviewer award
European Conference on Computer Vision (ECCV)2007

Best teaching assistant award
Department of Computer and Information Science, University of Ljubljana

2007

Best Ph.D. student presentation
Student competition at EU Cognition project meeting
http://www.vernon.eu/euCognition/six_monthly_meeting_2.htm
Presentation title: Learning Hierarchical Representations of Object Categories

2006

Award for the postgraduate studies
Department of Computer and Information Science, University of Ljubljana

2003

Best paper award
Austrian Association for Pattern Recognition (OAGM/AAPR) conference

2002

Award for the Diploma thesis
Department of Mathematics and Physics, University of Ljubljana

Professional Service
Co-founded
The Vector Institute, http://vectorinstitute.ai/
Program Chair
International Conference on 3D Vision: 2016
Area Chair
Computer Vision:
IEEE Computer Vision and Pattern Recognition (CVPR): 2016, 2017, 2018, 2021,
2022

European Conference in Computer Vision (ECCV): 2018
International Conference on Computer Vision (ICCV): 2017
Asian Conference on Computer Vision (ACCV): 2016
Machine Learning:
Neural Information Processing Systems (NIPS): 2017, 2018, 2019
International Conference on Learning Representations (ICLR): 2017, 2018, 2020
Natural Language Processing:
Empirical Methods on Natural Language Processing (EMNLP): 2016, 2017
Artificial Intelligence:
Association for the Advancement of Artificial Intelligence (AAAI): 2018
Chair
Workshop co-chair for CVPR’19
Tutorial co-chair for CVPR’16
Publication co-chair for ICCV 2015
Publication co-chair for CVPR 2015
Publication co-chair for CVPR 2014
Publication co-chair for CVPR 2013
Presentations chair for CVPR 2010
Multimedia chair for International Mathematical Olympiad 2006
Committees (at UofT)
Grad Affairs Committee (2018/2019)
Grad Visit Day (2016/2017)
Grad Visit Day (2015/2016)
DCS Grad Recruiting Committee (2014)
Grad Research Skills Committee (2014, 2015)
DCS Professional Master’s Admissions Committee (2014)
DCS Undergraduate Summer Research Program (2014, 2015)
Journal Reviewing
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)
International Journal of Computer Vision (IJCV)Computer Vision and Image Understanding (CVIU)
Robotics and Autonomous Systems (RAS)Pattern Recognition (PR)Image and Vision Computing (IMAVIS)
Program Committees
2009 – 2015

IEEE Computer Vision and Pattern Recognition (CVPR)

2009 – 2015

IEEE International Conference on Computer Vision (ICCV)2008 – 2016

European Conference on Computer Vision (ECCV)2013 – 2016

Neural Information Processing Systems (NIPS)

2015

International Conference on Robotics and Automation (ICRA)

2015

International Conference on Intelligent Robots and Systems (IROS)

2009

Asian Conference on Computer Vision (ACCV)

Tutorials and Workshops
2018

Tutorial on Computer Vision for Robotics and Driving
a half-day tutorial at CVPR’18, co-organized with Anelia Angelova
https://sites.google.com/view/visionroboticsdriving

2017

Role of Simulation in Computer Vision
Workshop at ICCV’17
https://www.microsoft.com/en-us/research/event/iccv-2017-role-of-simulation-in-computer-vision/

2017

Geometry Meets Deep Learning
Workshop at ICCV’17
https://sites.google.com/site/deepgeometry2017/2017

The Joint Video and Language Understanding Workshop: MovieQA and The Large
Scale Movie Description Challenge
Workshop at ICCV’17
https://sites.google.com/site/describingmovies/workshop-at-iccv-17

2017

PASCAL in Detail Workshop Challenge
Workshop at CVPR’17
https://sites.google.com/view/pasd

2016

Geometry Meets Deep Learning
Workshop at ECCV’16
https://sites.google.com/site/deepgeometry/

2015

Tutorial on 3D Indoor Scene Understanding
a half-day tutorial at CVPR’15, co-organized with Raquel Urtasun
http://www.cs.utoronto.ca/~fidler/3DsceneTutorialCVPR15.html

2014

Reconstruction Meets Recognition Challenge
Workshop at ECCV’14
http://cs.nyu.edu/~silberman/rmrc2014/index.php

2013

Reconstruction Meets Recognition Challenge
Workshop at ICCV’13
http://ttic.uchicago.edu/~rurtasun/rmrc/index.php

Teaching
2021

CSC 420 Intro to Image Understanding (undergraduate course), Fall session
http://www.cs.utoronto.ca/~fidler/teaching/2022/CSC420.html

2020

CSC 420 Intro to Image Understanding (undergraduate course), Fall session
http://www.cs.utoronto.ca/~fidler/teaching/2021/CSC420.html

2019

CSC 420 Intro to Image Understanding (undergraduate course), Fall session
http://www.cs.utoronto.ca/~fidler/teaching/2019Fall/CSC420.html

2019

CSC 420 Intro to Image Understanding (undergraduate course), Winter session
http://www.cs.utoronto.ca/~fidler/teaching/2019/CSC420.html

2018

CSC 2548 Machine Learning in Computer Vision (graduate course)
http://www.cs.utoronto.ca/~fidler/teaching/2018/CSC2548.html

2018

CSC 420 Intro to Image Understanding (undergraduate course)
http://www.cs.utoronto.ca/~fidler/teaching/2018/CSC420.html

2017

CSC 2539 Visual Recognition with Text (graduate course)
http://www.cs.utoronto.ca/~fidler/teaching/2017/CSC2539.html

2017

CSC 420 Intro to Image Understanding (undergraduate course)
http://www.cs.utoronto.ca/~fidler/teaching/2017/CSC420.html

2016

CSC 2523 Object Modeling and Recognition: Deep Learning in Computer Vision(graduate course)
http://www.cs.utoronto.ca/~fidler/teaching/2015/CSC2523.html

2015

CSC 420 Intro to Image Understanding (undergraduate course)
http://www.cs.utoronto.ca/~fidler/teaching/2015/CSC420.html

2015

CSC 2523 Object Modeling and Recognition: Visual Recognition with Text (graduate
course)http://www.cs.utoronto.ca/~fidler/CSC2523.html

2014

CSC 420 Intro to Image Understanding (undergraduate course)
http://www.cs.utoronto.ca/~fidler/CSC420.html(Awarded Professor of the Year by Computer Science Student Union at Universityof Toronto)
2013

2 lectures for the graduate course Computer Vision (taught by Prof. Raquel Urtasun
at TTI-C)

Invited lectures:Feb 2018

NextAI class on Computer VisionJan, May, 2017

NextAI class on Computer Vision

Mar 17, 2017

CogSci Academic Seminar at University of Toronto, Invited lecture on Perceptualmachines that see, communicate and reason

Mar 3, 2017

MBA statistics course at business school at University of Toronto, Invited lecture on
Machine Learning

Nov 30, 2016

CSC2503: Foundations of Computer Vision (graduate course in CS) at University ofToronto, Invited lecture on Neural Networks

Oct 5, 2016

ESC 301: Engineering Science Robotics Option seminar series (undergraduate course
in ECE) at University of Toronto, Invited lecture on Computer Vision

Supervision
Postdoctoral Fellow:
Makarand Tapaswi(Sept 2016 – Nov 2018)

Phd Students:
Xuanchi Ren(Sept 2022 – )Amir Mojtaba Sabour(Sept 2021 – )Towaki Takikawa

Co-supervised with Prof. Alec Jacobson(Sept 2020 – )Cheng Xie

Co-supervised with Prof. Florian Shkurti(Feb 2021 – )Frank Shen

(Feb 2021 – )Tianshi Cao(Feb 2021 – )Jun Gao(Jan 2020 – )Xiaohui Zeng

(Jan 2020 – )Huan Ling(Sept 2020 – )Ziang Wang

(Sept 2019 – )Jonah Philion(Sept 2019 – )Yuan-Hong Liao

(Sept 2019 – )Seung Kim

(Jan 2019 – )Amlan Kar

(Sept 2017 – )David Acuna(Sept 2018 – )Wenzheng Chen

Co-supervised with Prof. Kyros Kutulakos(Sept 2017 – )Maria Shugrina

Co-supervised with Prof. Karan Singh(Sept 2017 – Oct 2020)

Tingwu Wang

Co-supervised with Prof. Jimmy Ba(Sept 2016 – March 2021)

Hang Chu

Co-supervised with Prof. Raquel Urtasun(Sept 2016 – Feb 2020)

Kaustav Kundu

Co-supervised with Prof. Raquel Urtasun(Sept 2013 – ), now at Amazon

Tom Sie Ho Lee

Co-supervised with Prof. Sven Dickinson
Graduated in 2016

Msc Students:
Shirley Wang

(Sept 2022 – )Jeevan Devaranjan(Sept 2021 – Jan 2023)

Tianxing Li(Sept 2020 – Apr 2022)

Gary Leung(Sept 2020 – Apr 2022)

Sasha Doubov(Sept 2020 – Apr 2022)

Cheng Xie

Co-supervised with Prof. Florian Shkurti(Sept 2019 – Jan 2021)

Xinkai Wei(Sept 2019 – Jan 2021)

Tianshi Cao

Co-supervised with Prof. Richard Zemel(Sept 2019 – Jan 2021)

Henri Romel(Sept 2019 – Jan 2022)

Frank Shen

(Sept 2019 – Jan 2021)

Huan Ling(Sept 2018 – Jan 2020)

Jun Gao(Sept 2018 – Jan 2020), now PhD student at UofT

Xiaohui Zeng

(Sept 2018 – Jan 2020), now PhD student at UofT

Atef Chaudhury(Sept 2017 – June 2019), now at Google

Kevin Shen

(Sept 2017 – Jan 2019), now at Layer 6

Chaoqi Wang

(Sep 2017 – Jan 2019), now at Uber ATG

Jiaman Li(Sept 2017 – Jan 2019), now at USC

Seung Kim

(Sept 2017 – Jan 2019), now PhD student at UofT

Harris Chan

Co-supervised with Prof. Jimmy Ba(Sept 2017 – Jan 2019), now PhD student at UofT

Lluis Castrejon

Co-supervised with Prof. Raquel Urtasun(Sept 2015 – May 2017), now PhD student in University of Montreal

Yukun Zhu

Co-supervised with Prof. Raquel Urtasun and Prof. Ruslan Salakhutdinov(Sept 2014 – Jan 2016), now at Google

Ziyu Zhang

Co-supervised with Prof. Raquel Urtasun(Sept 2014 – May 2016), now at Snap Inc

Ivan Vendrov

Co-supervised with Prof. Raquel Urtasun(Sept 2014 – Jan 2016), now at Google

Abhishek Sen

Co-supervised with Prof. Raquel Urtasun
Graduated in 2013, now at Uber ATG

Undergraduate Students:
Sandeep Routray

3rd year undergraduate at IIT Kanpur
Date: June 2020 –Alex Bie

3rd year undergraduate at UWaterloo (internship)
Date: May 2020 – Oct 2020

Siyan Zhao

3rd year undergraduate at UofT (PEY internship)
Date: May 2020 – Sept 2020

Alex Zhang

3rd year undergraduate at UWaterloo (internship)
Date: Jan 2020 – Nov 2020

Avik Pal

3rd year undergraduate at IIT Kanpur (visiting student)
Date: Jan 2020 – Sept 2020

Hirotaka Ishihara

3rd year undergraduate at University of Toronto
Date: Nov 2019 – Sept 2020

Ziyue Xu

3rd year undergraduate at University of Toronto
Date: May 2020 – Dec 2020

Maciej Kowalski

3rd year visiting student at University of Toronto
Date: Nov 2019 – Sept 2020

Jiongtian Guo

3rd year undergraduate at University of Toronto
Date: Jan 2020 – April 2020

Chen Cui

3rd year undergraduate at University of Toronto
Date: May 2019 – April 2020

Zhaocong (Justin) Yuan 4th year undergraduate at University of Toronto
Date: Sept 2019 – May 2020
Jinchen Xuan

3rd year undergraduate at Peking University
Date: June 2019 – Nov 2019

Xi Yan

4th year undergraduate at University of Toronto (now a MSc student at
Stanford University)
Date: June 2019 – Dec 2019

Bowen Chen

4th year undergraduate at University of Toronto
Date: May 2019 – Dec 2019

Yinan Zhang

3rd year undergraduate at UWaterloo (internship, now MSc student at
Stanford)
Date: Jan 2019 – Aug 2019

Towaki Takikawa

3rd year undergraduate at UWaterloo (internship, now PhD student at UofT)
Date: Jan 2019 – Apr 2019

Jordi Fortuna

4th year undergraduate at UPC, Spain (visiting student)
Date: Sept 2018 – Feb 2019

Eric Guisado

4th year undergraduate at UPC, Spain (visiting student)
Date: Sept 2018 – Feb 2019

Xinkai Wei

3rd year undergraduate at UWaterloo (internship, now MSc student at UofT)
Date: Sep 2018 – Dec 2018

Yin (Calvin) Liu

3rd year undergraduate at UWaterloo (internship)
Date: Sep 2018 – Dec 2018

Zian Wang

3rd year undergraduate at Tsinghua University (now a PhD student at UofT)
Date: July 2018 – Nov 2018

Tianshi Cao

4th year undergraduate at University of Toronto (now a MSc student at UofT)
Date: July 2018 – May 2019

Huan Ling

4th year undergraduate at University of Toronto (now a PhD student at UofT)
Date: Oct 2016 –Yuhao Zhou

3rd year undergraduate at University of Toronto
Date: Jan 2017 –Ching-Yao Chuang

3rd year undergraduate at National Tsing Hua University (now a PhD student
at MIT)
Date: Aug 2017 – Dec 2017

Liren Chen

3rd year undergraduate at Tsinghua University (visiting student)
Date: June 2017 – Sept 2017

Tiantian (Ailsa) Fang

4th year undergraduate at University of Toronto (now a MSc student at UIUC)
Date: Sept 2017 – Dec 2017

Kefan (Arthur) Chen

4th year undergraduate at University of Toronto (now at Google
Date: June 2017 –Capstone projectDaiqing Li

4th year undergraduate at University of Toronto (now a Research Scientist at
NVIDIA)
Date: June 2017 – May 2018
Capstone project

Wenjia Liu

4th year undergraduate at University of Toronto
Date: June 2017 – May 2018
Capstone project

Jienan Yao

4th year undergraduate at University of Toronto
Date: Sept 2017 – May 2018
CSC494 (project course)Zheng Wu

3rd year undergraduate at Shanghai Jiao Tong University (visiting student)
Date: May 2017 – Sept 2017

Haokun Liu

3rd year undergraduate at Peking University (now MSc student at NYU)
Date: Feb 2017 – June 2017

Xavier Puig Fernandez

4th year undergraduate at UPC, Spain (now a PhD student at MIT)Co-supervised with Prof. Antonio Torralba
Date: Jan 2016 –Olga (Ge Ya) Xu

3rd year undergraduate at UofT
USRA’16
Date: June 2016 – Dec 2016

Kevin Kyunghwan Ra

4th year undergraduate at UofT (now a PhD student at McMaster University)
Date: Jan 2016 – May 2017

Amlan Kar

3rd year undergraduate at IIT Kanpur, India (now PhD student at UofT)
Co-supervised with Prof. Raquel Urtasun
Date: June 2016 – Aug 2016

Vasu Sharma

3rd year undergraduate at IIT Kanpur, India (now MSc student at CMU)
Co-supervised with Prof. Raquel Urtasun
Date: June 2016 – Aug 2016

Erin Grant

4th year undergraduate at UofT (now a PhD student at UC Berkeley)
Date: Jan 2016 – May 2016

Seung Kim

4th year undergraduate at UofT (now a MSc student at UofT)
Date: Jan 2016 – May 2016

Jurgen Aliaj

2nd year undergraduate at UofT (now a MSc student at UofT)
USRA’15, CSC494 (project course)
Date: June 2015 – Dec 2015

David Madras

4th year undergraduate at UofT (now a MSc student at UofT)
CSC494 (project course)
Date: Sept 2015 – Dec 2015

Nick Frosst

4th year undergraduate at UofT (now at Google)
CSC494 (project course)
Date: Sept 2015 – Dec 2015

Andrew Berneshawi

4th year undergraduate at UofT (now at Amazon)
CSC494 (project course): Road estimation with deep networks
Date: Jan 2015 – May 2015

Stanislav Ivashkevich

4th year undergraduate at UofT
CSC494 (project course): 3D object detection with branch and boundDate: Jan 2015 – April 2015

Taher Jafferjee

4th year undergraduate at UofT
CSC494 (project course): Solving jigsaw puzzles
Date: Sept 2014 – Dec 2014

Chenxi Liu

4th year undergraduate at Tsinghua University (now a PhD student at John
Hopkins)
Co-supervised with Prof. Raquel Urtasun.
Date: June 2014 – Nov 2014

Yinan Zhao

4th year undergraduate at Tsinghua University (now a PhD student at UTAustin)Co-supervised with Prof. Raquel Urtasun.
Date: June 2014 – Dec 2014

Jialiang Wang

4th year undergraduate at UofT (now a PhD student at Harvard University)
USRA’14, co-supervised with Prof. Sven Dickinson
Date: June 2014 – Aug 2014

Uri Priel

3rd year undergraduate at UofT
USRA’14, co-supervised with Prof. Sven Dickinson
Date: June 2014 – Aug 2014

Kamyar Ghasemipour

2nd year undergraduate at UofT (now a PhD student at UofT)
USRA’14, co-supervised with Prof. Suzanne Stevenson and Prof. Sven
Dickinson
Date: June 2014 – Aug 2014

Chen Kong

4th year undergraduate at Tsinghua University, (now a PhD student at CMU)
Co-supervised with Prof. Raquel Urtasun.
Date: June 2013 – March 2014

Ziyu Zhang

4th year undergraduate at Tsinghua University (now a Msc student at UofT)
Co-supervised with Prof. Raquel Urtasun
Date: August 2013 – June 2014

Meng Ye

4th year undergraduate at Beihang University
Co-supervised with Prof. Raquel Urtasun
Date: June 2013 – Nov 2013

Undergraduate Thesis Supervision:
Wesley Huang

4th year undergraduate at University of Toronto
Thesis title: Indoor navigation with visual targets
Date: Sept 2017 –Juan Morales Vega

4th year undergraduate at UPC
Thesis title: Object instance segmentation using recurrent models
Date: Feb 2017 – June 2017

Daniel Son

Thesis title: Labeling 3D CAD Scenes with 3D CNNs
Date: Sept 2016 - April 2016

Yiming Kang

Thesis title: Matching Houses in Streetview
Date: Sept 2015 - May 2016

Zexuang Wang

Co-supervised with Prof. Raquel Urtasun
Thesis title: Analyzing Table Tennis Games
Date: Sept 2015 - May 2016

Annie Ngai

Co-supervised with Prof. Sven Dickinson
Thesis title: Efficient Fine-grained Object Recognition and Pose Estimation
Date: Sept 2014 - April 2015

Sung BaikCo-supervised with Prof. Raquel Urtasun
Thesis title: Efficient Tracking by Detection
Date: Sept 2014 - April 2015

Visiting Msc/PhD Students:
Aisha Alaagib

PhD student in Sudan
Date: Sept 2019 – Jan 2020

Lara Dular

Phd student at University of Ljubljana
Date: July 2019 – Sept 2019

Bo DaiPhd student at CUHK
Date: Sept 2017 – May 2018

Enric Corona

Msc student at UPC
Date: May 2017 – Nov 2017

Ruiyu Li

Phd student at CUHK
Co-supervised with Prof. Raquel Urtasun
Date: May 2016 – Mar 2017

Shu Liu

Phd student at CUHK
Co-supervised with Prof. Raquel Urtasun
Date: May 2016 – Mar 2017

Zhi Luo

Msc student at Columbia University
Date: June 2016 – Dec 2016

Urban Jezernik

Phd student at University of Ljubljana
Co-supervised with Prof. Raquel Urtasun
Date: Jan 2016 – Apr 2016

Makarand Tapaswi

Phd student at Karlsruhe Institute of Technology (KIT)
Co-supervised with Prof. Raquel Urtasun
Date: Sept 2015 – Dec 2015

Roozbeh Mottaghi

Phd student at UCLA, now a postdoc at Stanford University
Co-supervised with Prof. Raquel Urtasun
Date: June 2012 – Nov 2013

Abhishek Sharma

PhD student at University of Maryland
Co-supervised with Prof. Raquel Urtasun.
Date: June 2012 – Nov 2012

Edgar Simo-Serra

PhD student at Institut de Robotica i Informatica Industrial
Co-supervised with Prof. Raquel Urtasun
Date: June 2013 –Nov 2013, June 2014 – Nov 2014

Liang-Chieh Chen

PhD student at UCLA
Co-supervised with Prof. Raquel Urtasun.
Date: August 2013 – Nov 2013

Press Coverage
Sept 2022

World-Class: NVIDIA Research Builds AI Model to Populate Virtual Worlds With
3D Objects, Characters: NVIDIA blog

Sept 2022

Reconstructing the Real World in DRIVE Sim With AI: NVIDIA blog

Aug 2022

Research Neural Fields Your Way with NVIDIA Kaolin Wisp: NVIDIA blog

June 2022

AI in the Big Easy: NVIDIA Research Lets Content Creators Improvise With 3D
Objects: NVIDIA blog

March 2022

NVIDIA Showcases Novel AI Tools in DRIVE Sim to Advance Autonomous Vehicle
Development: NVIDIA blog

March 2022

AI-Driven, Physics-Based Character Animation : GTC video

Jan 2022

High-precision Image Editing with AI: EditGAN: NVIDIA blog

Dec 2021

NVIDIA Research: Generating Motion Capture Animation Without Hardware or
Motion Data: NVIDIA blog

April 2021

Knight Rider Rides a GAN: Bringing KITT to Life With AI, NVIDIA Omniverse:
NVIDIA blog

May 2020

NVIDIA’s gameGAN re-creates Pacman on 40th Anniversary (featured in 250 news
articles worldwide): NVIDIA blog

Dec 2019

NVIDIA’s 3D DL library Kaolin: NVIDIA blog

Dec 2019

NVIDIA Researchers Bring Images to Life with AI: NVIDIA blog

June 2019NVIDIA’s STEAL AI gives neural networks better computer vision: Venturebeat

Nov 04, 2018

Our AI generated karaoke song appeared in the Simpsons episodeJune, 2018

NVIDIA: NVIDIA blog

Nov, 2017

UofT interview: UofT news

Sept, 2017

UofT article about the Elevate AI event: UofT news

July, 2017

Featured article in CVPR Daily News:
http://www.rsipvision.com/CVPR2017-Tuesday/
Best of CVPR:
http://www.rsipvision.com/ComputerVisionNews-2017August/#10

July, 2017

UofT DCS News: Best paper honorable mention at CVPR’17: link

Mar, 2017

UofT DCS News: Vector opening: link 1, link 2

Dec, 2016

H. Chu, R. Urtasun, S. Fidler, Song From PI: A Musically Plausible Network for Pop
Music Generation
News and tech websites:
The Register
The Star
GeekWire

The Guardian
MailOnline
Yahoo style!

New York Post
University of Toronto news
The Huffington Post

CTV News

radioEins

Television and radio:BBC
NPR
March, 2016

A comment for Globe an Mail about Microsoft’s bot Tay
www.theglobeandmail.com linkDec, 2015

Our paper on MovieQA has been featured in MIT Technological Review.

June-July, 2015 Neuroaesthetics in Fashion: Modeling the Perception of Beauty
Our CVPR’15 paper on fashion received a lot of attention from the media. It has
been featured in a number of News websites, Fashion magazines and International
news. We received numerous requests for interviews.
News websites:
New Scientist
Wired (UK)
Huffington Post UK
Protein
Daily Mail (UK)
GizmagQuartzMashableHuffington Post Canada
Yahoo (Canada)
PSFK
TheRecord.com

Tech Times
AOL News
MSN (Canada)
Science Daily
Toronto Star
iDigitalTimes

GlamourMarie Claire
Red Magazine (UK)
Styleite

Elle
Fashion Magazine
The Pool (UK)
Health Beauty Life

Woman (Spain)
Jetzt (Germany)
CenarioMT (Brazil)
Fashion Police (Nigeria)
Pressetext (Austria)

Stylebook (Germany)
Ansa (Italy)
Amsterdam Fashion (NL)
Nauka (Poland)
PopSugar (Australia)

Fashion websites / news:
Harper’s Bazaar
Cosmopolitan (UK)Yahoo style
FashionNotes
International news:
Vogue (Spain)
Wired (Germany)
La Gazzetta (Italy)
Marie Claire (France)
Pluska (Slovakia)
SinEmbargo (Mexico)
Television and radio:
RTVE (Spain)

Talks
Below is the list of my talks in the past few years:
1. NYU ECE Tandon speaker series
3D Generative AI, invite talk Oct 2023
2. Elevate Festival, main stage
Generative AI: the New Computing Era, keynote Sept 2023
3. Stanford Robotics Seminar
A.I. for 3D Content Creation, invited talk April 2023
4. GTC’23
Revolutionizing Autonomous Driving with Generative AI March 2023

5. GTC’22 Panel on Academia vs Entrepreneurship with Raquel Urtasun, Matthias Niessner, Jure
Leskovec
host
Sept 2022
6. GTC’22 Future of AI panel with Geoffrey Hinton, Yoshua Bengio and Yann Lecun
host
Sept 2022
7. Vector faculty retreat
Toronto Annotation Suite
Toronto, Sept 2022
8. University of Toronto, talk for high school students
Introduction to 3D Deep Learning
June 2022
9. NVIDIA SIGGRAPH keynote
Overview of NVIDIA’s latest AI research
Online video presentation, July 2022
10. CVPR’22 Workshop on Autonomous Driving
Neural Simulation, invited talk
New Orleans, June 2022
11. CVPR’22 Workshop on Synthetic Data
Neural Simulation, invited talk
New Orleans, June 2022
12. NVIDIA SIGGRAPH keynote
Overview of NVIDIA’s latest AI research
Online video presentation, July 2021
13. AI4ALL Invent the Future summer school (for girls grade 10-12)
Towards A.I.-driven Simulation, invited talkOnline talk, July 2021
14. BAIDU speaker series
Towards A.I.-driven Simulation, invited talkOnline talk, June 2021
15. CVPR’21 Workshop on 3D Deep Learning and Robotics
Towards A.I.-driven Simulation, invited talkOnline talk, June 2021
16. CVPR’21 Workshop on Autonomous Driving
Towards A.I.-driven Simulation, invited talkOnline talk, June 2021
17. CVPR’21 Workshop on Learning with Limited and Imperfect Data
Towards A.I.-driven Simulation, invited talkOnline talk, June 2021
18. CVPR’21 Workshop on Embodied AI
Towards A.I.-driven Simulation, invited talkOnline talk, June 2021

19. CVPR’21 Workshop on Robust Video Scene Understanding
Towards A.I.-driven Simulation, invited talkOnline talk, June 2021
20. CVPR’21 Workshop on Large Scale Holistic Video Understanding
Towards A.I.-driven Simulation, invited talkOnline talk, June 2021
21. Ontario Workshop in Computer Vision
A.I. for Content Creation, keynote
Online talk, April 2021
22. GTC
A.I. for Content Creation, invited talkOnline talk, April 2021
23. CMU seminar
A.I. for Content Creation, invited talkOnline talk, March 2021
24. Pinterest speaker series
A.I. Data Factory for A.I., invited talkOnline talk, Feb 2021
25. MIT class Introduction to Deep Learning
A.I. for Content Creation, invited lecture
Online talk, Jan 2021
26. ASEF speaker series
A.I. for Content Creation, invited talkOnline talk, Jan 2021
27. NeurIPS’20, Workshop on differentiable computer vision, graphics, and physics in machinelearning
A.I. for Content Creation, invited talkOnline talk, Dec 2020
28. GTC Fall
A.I. for Content Creation, invited talkOnline talk, Oct 2020
29. Techniche University of Munich (TUM), Seminar Series
A.I. for Content Creation, invited talkOnline talk, Sept 2020
30. BMVC
A.I. for Content Creation, keynote
Online talk, Aug 2020
31. Women in Computer Vision, ECCV’20
A.I. for Content Creation, invited talkOnline talk, Aug 2020
32. Task CV Workshop, ECCV’20
A.I. for Content Creation, invited talkOnline talk, Aug 2020

33. Workshop on Perception Through Structured Generative Models, ECCV’20
A.I. for Content Creation, invited talkOnline talk, Aug 2020
34. Invent the Future, Summer school on AI intended for grade 10 and 11 girls
A.I. for Content Creation, invite talkOnline talk, July 2020
35. AI City Workshop, CVPR’20
A.I. for Content Creation, invited talkOnline talk, June 2020
36. Workshop on AI for Content Creation, CVPR’20
A.I. for Content Creation, invited talkOnline talk, June 2020
37. Workshop on Learning 3D Generative Models, CVPR’20
A.I. for Content Creation, invited talkOnline talk, June 2020
38. SHAKER Pub Night, University of Toronto
A.I. Data Factory for A.I., invited talkToronto, Canada, Feb 2020
39. Workshop on Science meets Engineering in Deep Learning, NeurIPS’19
A.I. Data Factory for A.I., invited talkVancouver, Canada, Dec 2019
40. Workshop on Perception as generative reasoning, NeurIPS’19
A.I. Data Factory for A.I., invited talkVancouver, Canada, Dec 201941. Samsung AI Forum
A.I. Data Factory for A.I., invited talkSeoul, Korea, Nov 2019
42. Workshop on Geometry Meets Deep Learning, ICCV’19
Towards Automated 3D Content Creation, invited talkSeoul, Korea, Nov 2019
43. Workshop on Visual Recognition for Medical Images, ICCV’19
Towards Automated 3D Content Creation, invited talkSeoul, Korea, Oct 2019
44. Workshop on Scene Graph Representation and Learning, ICCV’19
Towards Automated 3D Content Creation, invited talkSeoul, Korea, Oct 201945. University of Washington
A.I. Data Factory for A.I., invited talkSeattle, USA, Oct 201946. Deep Learning Summer School organized by Autodesk
A.I. Data Factory for A.I., invited talkToronto, Canada, Aug 201947. APS360 at UofT
Towards Automated 3D Content Creation, invited lecture
Toronto, Canada, July 2019
48. CVSS summer school 2019
Towards Automated 3D Content Creation, invited lectureBlack Forest, Germany, July 201949. MILA
Towards Automated 3D Content Creation, invited talkMontreal, Canada, June 2019
50. Workshop on VQA and Dialog, CVPR’19
Compositional Reasoning Models, invited talkLong Beach, USA, June 2019
51. Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics, CVPR’19
Towards Automated 3D Content Creation, invited talkLong Beach, USA, June 201952. SUMO Workshop, CVPR’19
Towards Automated 3D Content Creation, invited talkLong Beach, USA, June 2019
53. Workshop on Conceptual Captions, CVPR’19
Compositional Reasoning Models, invited talkLong Beach, USA, June 2019
54. Workshop on Learning and Reasoning with Graph-Structured Representations,
ICML’19
Towards Automated 3D Content Creation, invited talkLong Beach, USA, June 2019
55. Workshop on Human in the Loop Learning, ICML’10
Teaching Machines with Human in the Loop, invited talkLong Beach, USA, June 201956. AI World Forum
A.I. Data Factory for A.I., keynote
Toronto, Canada, March 201957. a2-dlearn 2019
A.I. Data Factory for A.I., invited talk
Ann Arbor, USA, Feb 201958. MIE 324 guest lecture at UofT
Teaching machines to see, communicate, and act
Toronto, Canada, Oct 201859. POCV workshop, co-located with ECCV’18
Teaching machines to see, communicate, and act, invited talkMunich, Germany, Sept 2018
60. Visual Learning and Embodied Agents in Simulation Environments workshop, co-locatedwith ECCV’18
VirtualHome: Representing Activities via Programs, invited talkMunich, Germany, Sept 2018

61. Talk for interns at NVIDIA
Teaching Machines with Humans in the Loop
Santa Clara, USA, Aug 201862. Medical Imaging Summer School (MISS’18)
Learning with Less Supervision
Favignana, Italy, July 201863. Deep Learning Summer School (DLSS’18)
Deep Learning for Computer Vision
Toronto, Canada, July 2018
64. Amazon’s Computer Vision Conference
Fashion Synthesis and Retrieval
Seattle, USA, April 2018
65. GTC’18 conference
Teaching Machines with Humans in the Loop
San Jose, USA, March 2018
66. CVPR’18 AC meeting
Efficient Object Annotation with Polygon-RNN
Toronto, Canada, Feb 2018
67. New Deep Learning Techniques (DLT’18), Institute for Pure and Applied Mathematics
Teaching Machines with Humans in the Loop, invited talkLos Angeles, USA, Feb 2018
68. University of Toronto, Mechanical Engineering Dept.
Towards machines that see, communicate, and act, invited talkToronto, Canada, Dec 2017
69. VIGIL workshop, co-located with NIPS’17
Teaching machines to see, communicate, and act, invited talkLong Beach, USA, Dec 2017
70. CIFAR workshop, co-located with NIPS’17
Interactive Annotation with Polygon-RNN, invited talkLong Beach, USA, Dec 201771. NVIDIA at UofT event
Teaching machines to see, communicate, and act, invited talkToronto, Canada, Nov 2017
72. Re-work Deep Learning Summit
Teaching machines to see, communicate, and act, invited talkMontreal, Canada, Oct 2017
73. Facebook Faculty Summit
Teaching machines to see, communicate, and act, invited talk
New York City, USA, Oct 201774. University of Toronto, Engineering Dept.
Towards machines that see, communicate, and act, invited talkToronto, Canada, Sept 201775. Acivs conference
Towards machines that see, communicate, and act, invited talk
Antwerp, Belgium, Sept 201776. Elevate AI, http://elevatetoronto.com/event/elevate-ai/
Machine Vision, lighting talkToronto, Canada, Sept 2017
77. ACL’17 Workshop on Representation Learning for NLP
Learning Joint Embeddings of Vision and Language, keynote
Vancouver, Canada, August 2017
78. CVPR’17 Workshop on Deep-Vision: Deep Learning in Computer Vision
Towards perceptual machines that parse, communicate, and act, invited talk
Honolulu, Hawaii, July 2017
79. CVPR’17 Workshop on Visual Question Answering Challenge
Teaching machines via natural language feedback, invited talk
Honolulu, Hawaii, July 2017
80. CVPR’17 Workshop on Continuous and Open-Set Learning
Learning Joint Embeddings of Vision and Language, keynote
Honolulu, Hawaii, July 201781. International Computer Vision Summer School (ICVSS’17)
Learning Joint Embeddings of Vision and Language, invited lecture
Sicily, Italy, July 2017
82. Google faculty summit
Towards perceptual machines that parse, communicate, and act, invited talkZurich, Switzerland, July 2017
83. Women in Robotics seminar series
Towards perceptual machines that see, communicate, and reason, invited talkToronto, Canada, May 201784. Deep Learning Summit by Re-Work
Towards understanding stories in videos, invited talkBoston, USA, May 201785. Seminar at MERL (Mitsubishi Electric Research Lab)
Learning Joint Embeddings of Images and Language, invited talkBoston, USA, May 201786. NVIDIA’s GPU Technology Conference (GTC’17)
Towards understanding stories in videos, invited talkAccording to GTC participant survey: Speaker rating: 5/5, content rating 4.88/5
Palo Alto, USA, May 201787. CogSci seminar at University of Toronto
Towards understanding stories in videos, invited talkToronto, Canada, March 201788. Seminar at Qualcomm
Learning Joint Embeddings of Images and Language, invited talkMarkham, Canada, March 201789. TedX@UofT at University of Toronto
Towards understanding stories in videos, invited talkToronto, Canada, Feb 2017
90. Robust Vision Symposium at MPI Tuebingen
Learning Embeddings of Images and Language, invited talkTuebingen, Germany, Jan 2017
91. Fields ML Seminar
Learning Embeddings of Images and Language, invited talkToronto, Canada, Nov 2016
92. AI night at University of Toronto
Towards understanding stories in videos, invited talkToronto, Canada, Nov 2016
93. Workshop on Storytelling with Images and Videos at ECCV’16
Learning Embeddings of Images and Language, invited talk
Amsterdam, Netherlands, October 2016
94. Joint Imagenet and MS Coco Visual Recognition Challenge Workshop at ECCV’16
Learning Embeddings of Images and Language, keynote
Amsterdam, Netherlands, October 2016
95. CogSci graduate orientation day at University of Toronto
The Gee-Whiz of A.I., invited talkToronto, Canada, Sep 2016
96. ACCV’16 Area Chair meeting
Towards Understanding Stories from Videos, talkTaipei, Taiwan, August 2016
97. Deep Learning Workshop at ICML’16
Towards Understanding Stories from Videos, invited talk
New York City, US, June 2016
98. Carnegie Mellon University
Towards Understanding Stories from Videos, invited talk (vision seminar)
Pittsburgh, US, April 2016
99. University of Pittsburgh
Towards Understanding Stories from Videos, invited talk (vision seminar)
Pittsburgh, US, April 2016
100. University of Toronto
Towards perceptual machines that see, communicate, and reason, invited talkToronto, Canada, March 2016
101. CVPR’16 Area Chair meeting
Towards Understanding Stories from Videos, talkVancouver, Canada, February 2016
102. York University
Towards Understanding Stories from Videos, invited talkToronto, Canada, January 2016

103. Scenes From Video (SFV), Workshop in conjunction with ICCV’15
Towards Story-like Descriptions by Watching Movies and Reading Books, invited talkSantiago, Chile, December 2015
104. Closing the Loop Between Vision and Language, Workshop at ICCV’15
Towards Story-like Descriptions by Watching Movies and Reading Books, invited talkSantiago, Chile, December 2015
105. Describing and Understanding Video & The Large Scale Movie Description Challenge(LSMDC), Workshop at ICCV’15
Towards Story-like Descriptions by Watching Movies and Reading Books, invited talkSantiago, Chile, December 2015
106. 3D Scene Understanding, Workshop at ICCV’15
3D Indoor Scene Understanding and Localization, invited talkSantiago, Chile, December 2015
107. Women in Computer Vision, Workshop at CVPR’15
Understanding Complex Scenes and People That Talk About Them, invited talkBoston, US, June 2015
108. Symposia at CRV’15
Understanding Complex Scenes and People That Talk About Them, invited talk
Halifax, Canada, June 2015
109. Deep Learning for Vision, Workshop at DALI’15
Scene Understanding or How I Grew To Like Deep Learning, invited talkCanary Islands, Spain, April 2015110. University of Pennsylvania
Understanding Complex Scenes and People That Talk About Them, invited talk at the GRASP
vision seminar
Philadelphia, US, March 2015
111. Daghstuhl Workshop on Holistic Scene Understanding
Understanding Complex Scenes and People That Talk About Them, invited talkDaghstuhl, Germany, February 2015112. Karlsruhe Institute of Technology
Understanding Complex Scenes and People That Talk About Them, invited talkKarlsruhe, Germany, February 2015
113. The Hong Kong Polytechnic University
Understanding Complex Scenes and People That Talk About Them, invited talkHong Kong, December 2014114. AI Night, University of Toronto
Understanding Complex Scenes and People That Talk About Them, invited talkToronto, Canada, November 2014
115. University of Ljubljana
Computer Vision, invited lecture for a math summer school
Ljubljana, Slovenia, August 2014116. International Conference on Computer Vision (ICCV) 2013
Holistic Scene Understanding for 3D Object Detection with RGBD cameras, oral presentation
Sydney, Australia, December 2013

117. MPI Tuebingen, Perceiving Systems
2D and 3D object detection by exploiting segmentation and contextual information, invited talkTuebingen, Germany, September 2013
118. Microsoft Research
2D and 3D object detection by exploiting segmentation and contextual information, invited talkCambridge, UK, September 2013119. Midwest Vision Workshop
Bottom-up Segmentation for Top-down Detection
Chicago, US, May 2013

Publications
Journal Articles and Book Chapters
1. D. Damen, H. Doughty, G. Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J. Munro, T.
Perrett, W. Price, M. Wray. The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines.
Trans. on Pattern Analysis and Machine Intelligence, 2020.
2. B. Zhou, H. Zhao, X. Puig, T. Xiao, S. Fidler, A. Barriuso, A. Torralba. Semantic Understandingof Scenes Through the ADE20K Dataset. International Journal of Computer Vision, 2018
3. X. Chen, K. Kundu, Y. Zhu, H. Ma, S. Fidler, R. Urtasun. 3D Object Proposals using Stereo
Imagery for Accurate Object Class Detection. Trans. on Pattern Analysis and Machine Intelligence,2017
4. R. Mottaghi, A. Yuille, S. Fidler, R. Urtasun, D. Parikh. Human-Machine CRFs for Identifying
Bottlenecks in Scene Understanding. Trans. on Pattern Analysis and Machine Intelligence, Vol. 38,
Num. 1, pp 74-87, 2016.
5. T. Lee, S. Fidler, A. Levinshtein, C. Sminchisescu, S. Dickinson, A Framework for Symmetric Part
Detection in Cluttered Scenes, MDPI Symmetry, Vol. 7, pp 1333-1351, 2015.
6. M. Fritz, M. Andriluka, S. Fidler, M. Stark, A. Leonardis, B. Schiele. Categorial Perception. In:
Cognitive Systems, Editors: H. I. Christensen, G.-J. Kruijff, A. Sloman and J. Wyatt, Springer, 2010.
7. S. Fidler, M. Boben, A. Leonardis. Learning Hierarchical Compositional Representations of Object Structure. In: Object Categorization: Computer and Human Vision Perspectives, Editors: S.
Dickinson, A. Leonardis, B. Schiele and M. J. Tarr, Cambridge University Press, 2009.
8. L. Furst, S. Fidler, A. Leonardis. Selecting features for object detection using an AdaBoostcompatible evaluation function. Pattern Recognition Letters (PRL), 2008, vol. 29, no. 11, pp.
1603 − 1612.
9. S. Fidler, D. Skočaj, A. Leonardis. Combining Reconstructive and Discriminative Subspace Methods for Robust Classification and Regression by Subsampling. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE PAMI), 2006, vol. 28(3), pp. 337 − 350.

Conference Papers
1. Zian Wang*, Tianchang Shen*, Merlin Nimier-David*, Nicholas Sharp, Jun Gao, Alexander Keller,
Sanja Fidler, Thomas Müller, Zan Gojcic. Adaptive Shells for Efficient Neural Radiance Field
Rendering. SIGGRAPH Asia, 2023
2. Towaki Takikawa, Thomas Müller, Merlin Nimier-David, Alex Evans, Sanja Fidler, Alec Jacobson,
Alexander Keller. Compact Neural Graphics Primitives with Learned Hash Probing. SIGGRAPH
Asia, 2023
3. Tianshi Cao, Karsten Kreis, Sanja Fidler, Nicholas Sharp*, Kangxue Yin*. TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models. In International Conference on Computer
Vision (ICCV), 2023, oral presentation.
4. Daiqing Li*, Huan Ling*, Amlan Kar, David Acuna, Seung Wook Kim, Karsten Kreis, Antonio Torralba, Sanja Fidler. DreamTeacher: Pretraining Image Backbones with Deep Generative Models.
In International Conference on Computer Vision (ICCV), 2023

5. Shengyu Huang, Zan Gojcic, Zian Wang, Francis Williams, Yoni Kasten, Sanja Fidler, Konrad
Schindler, Or Litany. Neural LiDAR Fields for Novel View Synthesis. In International Conference
on Computer Vision (ICCV), 2023
6. Tzofi Klinghoffer, Jonah Philion, Wenzheng Chen, Or Litany, Zan Gojcic, Jungseock Joo, Ramesh
Raskar, Sanja Fidler, Jose Alvarez. Towards Viewpoint Robustness in Bird’s Eye View Segmentation. In International Conference on Computer Vision (ICCV), 2023
7. Jonathan Lorraine, Kevin Xie, Xiaohui Zeng, Chen-Hsuan Lin, Towaki Takikawa, Nicholas Sharp,
Tsung-Yi Lin, Ming-Yu Liu, Sanja Fidler, James Lucas. ATT3D: Amortized Text-to-3D Object
Synthesis. In International Conference on Computer Vision (ICCV), 2023
8. Jingbo Wang, Ye Yuan, Zhengyi Luo, Kevin Xie, Dahua Lin, Umar Iqbal, Sanja Fidler, Sameh
Khamis. Learning Human Dynamics in Autonomous Driving Scenarios. In International Conference
on Computer Vision (ICCV), 2023
9. Yanwei Li, Zhiding Yu, Jonah Philion, Animashree Anandkumar, Sanja Fidler, Jiaya Jia, Jose
Alvarez. End-to-end 3D Tracking with Decoupled Queries. In International Conference on Computer
Vision (ICCV), 2023
10. Haotian Zhang, Ye Yuan, Viktor Makoviychuk, Yunrong Guo, Sanja Fidler, Xue Bin Peng, Kayvon
Fatahalian. Learning Physically Simulated Tennis Skills from Broadcast Videos. SIGGRAPH 2023,
best paper honorable mention.
11. Mohamed Hassan, Yunrong Guo, Tingwu Wang, Michael Black, Sanja Fidler, Xue Bin Peng.
Synthesizing Physical Character-Scene Interactions. SIGGRAPH 2023
12. Tianchang Shen, Jacob Munkberg, Jon Hasselgren. Kangxue Yin, Zian Wang, Wenzheng Chen, Zan
Gojcic, Sanja Fidler, Nicholas Sharp*, Jun Gao*. Flexible Isosurface Extraction for Gradient-BasedMesh Optimization. SIGGRAPH 2023
13. Viraj Uday Prabhu, David Acuna, Rafid Mahmood, Marc T. Law, Yuan-Hong Liao, Judy Hoffman, Sanja Fidler, James Lucas. Bridging the Sim2Real gap with CARE: Supervised Detection
Adaptation with Conditional Alignment and Reweighting. TMLR, 2023
14. Jiahui Huang, Zan Gojcic, Matan Atzmon, Or Litany, Sanja Fidler, Francis Williams. Neural
Kernel Surface Reconstruction. In Computer Vision and Pattern Recognition (CVPR), 2023, oralpresentation.
15. Chen-Hsuan Lin*, Jun Gao*, Luming Tang*, Towaki Takikawa*, Xiaohui Zeng*, Xun Huang,
Karsten Kreis, Sanja Fidler, Ming-Yu Liu, Tsung-Yi Lin: Magic3D: High-Resolution Text-to-3D
Content Creation. In Computer Vision and Pattern Recognition (CVPR), 2023.
16. Zian Wang, Tianchang Shen, Jun Gao, Shengyu Huang, Jacob Munkberg, Jon Hasselgren, Zan
Gojcic, Wenzheng Chen, Sanja Fidler. Neural Fields meet Explicit Geometric Representations for
Inverse Rendering of Urban Scenes. In Computer Vision and Pattern Recognition (CVPR), 2023
17. Seung Wook Kim*, Bradley Brown*, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin
Rombach, Antonio Torralba, Sanja Fidler. NeuralField-LDM: Scene Generation with Hierarchical
Latent Diffusion Models. In Computer Vision and Pattern Recognition (CVPR), 2023.
18. Andreas Blattmann*, Robin Rombach*, Huan Ling*, Tim Dockhorn, Seung Wook Kim, Sanja
Fidler, Karsten Kreis. Align your Latents: High-Resolution Video Synthesis with Latent Diffusion
Models. In Computer Vision and Pattern Recognition (CVPR), 2023.
19. Davis Rempe*, Zhengyi Luo*, Xue Bin Peng, Ye Yuan, Kris M. Kitani, Karsten Kreis, Sanja Fidler,
Or Litany. Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion. In
Computer Vision and Pattern Recognition (CVPR), 2023.

20. Maria Shugrina, Chin-Ying Li, Sanja Fidler. Neural Brushstroke Engine: Learning a Latent StyleSpace of Interactive Artistic Tools. SIGGRAPH Asia, 2022
21. Jordan Juravsky, Yunrong Guo, Sanja Fidler, Xue Bin Peng. PADL: Language-Directed PhysicsBased Character Control. SIGGRAPH Asia, 2022.
22. Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan
Gojcic, Sanja Fidler. GET3D: A Generative Model of High Quality 3D Textured Shapes Learned
from Images. In Neural Information Processing Systems (NeurIPS), 2022.
23. Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis.
LION: Latent Point Diffusion Models for 3D Shape Generation. In Neural Information Processing
Systems (NeurIPS), 2022.
24. Rafid Mahmood, James Lucas, Jose Alvarez, Sanja Fidler, Marc Law. Optimizing Data Collection
for Machine Learning. In Neural Information Processing Systems (NeurIPS), 2022.
25. Zian Wang, Wenzheng Chen, David Acuna, Jan Kautz, Sanja Fidler. Neural Light Field Estimation for Outdoor Scenes with Differentiable Virtual Object Insertion. In European Conference on
Computer Vision (ECCV), 2022.
26. Gopal Sharma, Kangxue Yin, Subhransu Maji, Evangelos Kalogerakis, Or Litany, Sanja Fidler.
MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation. In European Conference on Computer Vision (ECCV), 2022.
27. Hassan Abu Alhaija, Alara Dirik, Andre Knorig, Sanja Fidler, Maria Shugrina. XDGAN: MultiModal 3D Shape Generation in 2D Space. In British Machine Vision Conference (BMVC), 2022.
28. X. B. Peng, Y. Guo, L. Halper, S. Levine, S. Fidler. ASE: Large-Scale Reusable Adversarial Skill
Embeddings for Physically Simulated Characters. SIGGRAPH’22 – journal
29. Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson, S. Fidler, Or Litany. Learning Smooth
Neural Functions via Lipschitz Regularization. SIGGRAPH’22 – conference.
30. Towaki Takikawa, Alex Evans, Jonathan Tremblay, Thomas Müller, Morgan McGuire, Alec Jacobson,
S. Fidler. Variable Bitrate Neural Fields. SIGGRAPH’22 – conference.
31. Zhiqin Chen, Kangxue Yin, S. Fidler. AUV-Net: Learning Aligned UV Maps for Texture Transfer
and Synthesis. In Computer Vision and Pattern Recognition (CVPR), 2022.
32. Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, ThomasMüller, S. Fidler. Extracting Triangular 3D Models, Materials, and Lighting From Images. In
Computer Vision and Pattern Recognition (CVPR), 2022, oral presentation.
33. Seung Wook Kim, Karsten Kreis, Daiqing Li, Antonio Torralba, S. Fidler. Polymorphic-GAN:
Generating Aligned Samples across Multiple Domains with Learned Morph Maps. In Computer
Vision and Pattern Recognition (CVPR), 2022, oral presentation.
34. Davis Rempe, Jonah Philion, Leonidas Guibas, S. Fidler, Or Litany. Generating Useful AccidentProne Driving Scenarios via a Learned Traffic Prior. In Computer Vision and Pattern Recognition(CVPR), 2022.
35. Daiqing Li, Huan ling, Seung Wook Kim, Karsten Kreis, S. Fidler, Antonio Torralba. BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations. In Computer Vision and Pattern Recognition (CVPR), 2022.
36. Rafid Mahmod, James Lucas, David Acuna, Daiqing Li, Jonah Philion, Jose Alvarez, Zhiding Yu, S.
Fidler, Marc T Law. How Much More Data Do I Need? Estimating Requirements for Downstream
Tasks. In Computer Vision and Pattern Recognition (CVPR), 2022.

37. Matan Atzmon, Koki Nagano, S. Fidler, Sameh Khamis, Yaron Lipman. Frame Averaging forEquivariant Shape Space Learning. In Computer Vision and Pattern Recognition (CVPR), 2022.
38. Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, S. Fidler, Or Litany.
Neural fields as learnable kernels for 3D reconstruction. In Computer Vision and Pattern Recognition(CVPR), 2022.
39. D.Acuna, G. Zhang, M. T Law, S. Fidler. Domain Adversarial Training: A Game Perspective. In
International Conference on Learning Representations (ICLR), 2022.
40. R. Mahmood, M.T. Law, S. Fidler. Low Budget Active Learning via Wasserstein Distance: An
Integer Programming Approach. In International Conference on Learning Representations (ICLR),
2022.
41. H. Ling, K. Kreis, D. Li, S. Wook Kim, A. Torralba, S. Fidler. EditGAN: High-Precision Semantic
Image Editing. In Neural Information Processing Systems (NeurIPS), 2021.
42. T. Shen, J. Gao, K. Yin, M.-Y. Liu, S. Fidler. Deep Marching Tetrahedra: a Hybrid Representationfor High-Resolution 3D Shape Synthesis. In Neural Information Processing Systems (NeurIPS), 2021.
43. W. Chen, J. Litalien, J. Gao, Z. Wang, C. Fuji Tsang, S. Khamis, O. Litany, S. Fidler. DIB-R++:
Learning to Disentangle Material from Lighting Using a Deferred Image-based Renderer. In Neural
Information Processing Systems (NeurIPS), 2021.
44. D. Paschalidou, A. Kar, M. Shugrina, K. Kreis, A. Geiger, S. Fidler. ATISS: Autoregressive Transformers for Indoor Scene Synthesis. In Neural Information Processing Systems (NeurIPS), 2021.
45. D. Acuna, Jonah Philion, S. Fidler. Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation. In Neural Information Processing Systems (NeurIPS), 2021.
46. T. Cao, S. Doubov, D. Acuna, S. Fidler. Scalable Neural Data Server: A Data Recommender forTransfer Learning. In Neural Information Processing Systems (NeurIPS), 2021.
47. T. Cao, A. Bie, A. Vahdat, S. Fidler, K. Kreis. Don’t Generate Me: Training Differentially Private
Generative Models with Sinkhorn Divergence. In Neural Information Processing Systems (NeurIPS),
2021.
48. Z. Wang, J. Philion, S. Fidler, J. Kautz. Learning Indoor Inverse Rendering with 3D SpatiallyVarying Lighting. In International Conference on Computer Vision (ICCV), 2021, oral presentation.
49. K. Yin, J. Gao, M. Shugrina, S. Khamis, S. Fidler. 3DStyleNet: Creating 3D Shapes with Geometricand Texture Style Variations. In International Conference on Computer Vision (ICCV), 2021, oralpresentation.
50. K. Xie, T. Wang, U. Iqbal, S. Fidler, F. Shkurti. Physics-based Human Motion Estimation and
Synthesis from Videos. In International Conference on Computer Vision (ICCV), 2021.
51. D. Acuna, G. Zhang, M. T Law, S. Fidler. f-Domain-Adversarial Learning: Theory and Algorithms
for Unsupervised Domain Adaptation with Neural Networks. In International Conference on Machine
Learning (ICML), 2021.
52. N. Chang, Z. Yu, Y.-X. Wang, A. Anandkumar, S. Fidler, J. M. Alvarez. Image-Level or ObjectLevel? A Tale of Two Resampling Strategies for Long-Tailed Detection. In International Conference
on Machine Learning (ICML), 2021.
53. T. Takikawa, J. Litalien, K. Yin, K. Kreis, C. Loop, A. Jacobson, D. Nowrouzezahrai, M. McGuire,
S. Fidler. Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes. In
Computer Vision and Pattern Recognition (CVPR), 2021, oral presentation.

54. S. Kim, J. Philion, A. Torralba, S. Fidler. DriveGAN: Towards a Controllable High-Quality Neural
Simulation. In Computer Vision and Pattern Recognition (CVPR), 2021, oral presentation.
55. Y. Zhang, H. Ling, J. Gao, K. Yin, J.-F. Lafleche, A. Barriuso, A. Torralba, S. Fidler. DatasetGAN: Efficient labeled data factory with minimal human effort. In Computer Vision and Pattern
Recognition (CVPR), 2021, oral presentation.
56. Y.-H. Liao, A. Kar, S. Fidler. Towards good practices for efficiently annotating large-scale image
classification datasets. In Computer Vision and Pattern Recognition (CVPR), 2021, oral presentation.
57. D. Li, J. Yang, K. Kreis, A. Torralba, S. Fidler. Semantic Segmentation with Generative Models: Semi-supervised Learning and Strong Out-of-Domain Generalization. In Computer Vision and
Pattern Recognition (CVPR), 2021.
58. D. Paschalidou, A. Katharopoulos, Andreas Geiger, S. Fidler. Neural Parts: Learning Expressive 3D
Shape Abstractions with Invertible Neural Networks. In Computer Vision and Pattern Recognition(CVPR), 2021.
59. Y. Zhang, W. Chen, H. Ling, J. Gao, Y. Zhang, A. Torralba, S. Fidler. Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering. In International
Conference on Learning Representations (ICLR), 2021, oral presentation.
60. X. Puig, T. Shu, S. Li, Z. Wang, Y.-H. Liao, J. B. Tenenbaum, S. Fidler, A. Torralba. WatchAnd-Help: A Challenge for Social Perception and Human-AI Collaboration. Best paper award at
Cooperative AI Workshop at NeurIPS’20. In International Conference on Learning Representations(ICLR), 2021, spotlight presentation.
61. A. Pal, J. Philion, Y.-H. Liao, S. Fidler. Emergent Road Rules In Multi-Agent Driving Environments. In International Conference on Learning Representations (ICLR), 2021.
62. K. Murthy Jatavallabhula, M. Macklin, F. Golemo, V. Voleti, L. Petrini, M. Weiss, B. Considine,
J. Parent-Lévesque, K. Xie, K. Erleben, L. Paull, F. Shkurti, D. Nowrouzezahrai, S. Fidler. gradSim: Differentiable simulation for system identification and visuomotor control. In International
Conference on Learning Representations (ICLR), 2021.
63. M. Zhang, K. Sapra, S. Fidler, S. Yeung, J. M. Alvarez. Personalized Federated Learning with First
Order Model Optimization. In International Conference on Learning Representations (ICLR), 2021.
64. J. Gao, W. Chen, T. Xiang, A. Jacobson, M. McGuire, S. Fidler. Learning Deformable Tetrahedral
Meshes for 3D Reconstruction. In Neural Information Processing Systems (NeurIPS), 2020.
65. H. Ling, D. Acuna, K. Kreis, S. Kim, S. Fidler. Variational Amodal Object Completion for Interactive Scene Editing. In Neural Information Processing Systems (NeurIPS), 2020.
66. J. Philion, S. Fidler. Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly
Unprojecting to 3D. In European Conference on Computer Vision (ECCV), 2020.
67. J. Devaranjan, A. Kar, S. Fidler. Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation. In European Conference on Computer Vision (ECCV), 2020.
68. J. Gao, Z. Wang, S. Fidler. Beyond Fixed Grid: Learning Geometric Image Representation with a
Deformable Grid. In European Conference on Computer Vision (ECCV), 2020.
69. F. Shen, J. Gao, A. Kar, S. Fidler. Interactive Annotation of 3D Object Geometry. In European
Conference on Computer Vision (ECCV), 2020.
70. B. Chen, H. Ling, X. Zeng, J. Gao, S. Fidler. Interactive Annotation Framework for Video Object
Segmentation. In European Conference on Computer Vision (ECCV), 2020.

71. H. Chu, S. Ma, F. Torre, S. Fidler, Y. Sheikh. Expressive Telepresence via Modular Codec Avatar.
In European Conference on Computer Vision (ECCV), 2020.
72. D. Li, A. Kar, N. Ravikumar, A. Frangi, S. Fidler. Federated Simulation for Medical Imaging. In
Medical Image Computing and Computer Assisted Interventions (MICCAI), 2020. Nominated forYoung Scientist Award
73. M. Shugrina, A. Kar, S. Fidler, K. Singh. Nonlinear Color Triads for Approximation, Learning andDirect Manipulation of Color Distributions. In SIGGRAPH, 2020.
74. X. Yan, D. Acuna, S. Fidler. Neural Data Server: A Large-Scale Search Engine for Transfer
Learning Data. In Computer Vision and Pattern Recognition (CVPR), 2020, oral presentation.
75. J. Philion, A. Kar, S. Fidler. Learning to Evaluate Perception Models using Planner-Centric Metrics. In Computer Vision and Pattern Recognition (CVPR), 2020.
76. S. Kim, J. Philion, A. Torralba, S. Fidler. Learning to Simulate Dynamic Environments with
GameGAN. In Computer Vision and Pattern Recognition (CVPR), 2020.
77. W. Chen, P. Mirdehghan, S. Fidler, Kyros Kutulakos. Auto-Tuning Structured Light. In Computer
Vision and Pattern Recognition (CVPR), 2020.
78. T. Cao, M. Law, S. Fidler. A Theoretical Analysis of the Number of Shots in Few-Shot Learning.
In International Conference on Learning Representations (ICLR), 2020.
79. W. Yu, Y. Lu, S. Easterbrook, S. Fidler. Efficient and Information-Preserving Future Frame Prediction and Beyond. In International Conference on Learning Representations (ICLR), 2020.
80. W. Chen, J. Gao, H. Ling, E. Smith, J. Lehtinen, A. Jacobson, S. Fidler. Learning to Predict
3D Objects with an Interpolation-based Differentiable Renderer. In Neural Information Processing
Systems (NeurIPS), 2019.
81. A. Kar, A. Prakash, M.-Y. Liu, E. Cameracci, J. Yuan, M. Rusiniak, D. Acuna, A. Torralba, S. Fidler. Meta-Sim: Learning to Generate Synthetic Datasets. In International Conference on Computer
Vision (ICCV), 2019, oral presentation.
82. H. Chu, D. Li, D. Acuna, A. Kar, M. Shugrina, X. Wei, M.-Y. Liu, A. Torralba, S. Fidler. Neural
Turtle Graphics for Modeling City Road Layouts. In International Conference on Computer Vision(ICCV), 2019, oral presentation.
83. T. Takikawa, D. Acuna, V. Jampani, S. Fidler. Gated-SCNN: Gated Shape CNNs for Image
Segmentation. In International Conference on Computer Vision (ICCV), 2019.
84. K. Shen, A. Kar, S. Fidler. Lifelong Learning for Image Captioning by Asking Natural Language
Questions. In International Conference on Computer Vision (ICCV), 2019.
85. M. Tapaswi, M.Law, S. Fidler. Video Face Clustering with Unknown Number of Clusters. In
International Conference on Computer Vision (ICCV), 2019.
86. X. Zeng, R. Liao, L. Gu, Y. Xiong, S. Fidler, R. Urtasun. DMM-Net: Differentiable Mask-Matching
Network for Video Instance Segmentation. In International Conference on Computer Vision (ICCV),2019.
87. D. Acuna, A. Kar, S. Fidler. Devil is in the Edges: Learning Semantic Boundaries from Noisy
Annotations. In Computer Vision and Pattern Recognition (CVPR), 2019, oral presentation.
88. H. Ling, J. Gao, A. Kar, W. Chen, S. Fidler. Fast Interactive Object Annotation with Curve-GCN.
In Computer Vision and Pattern Recognition (CVPR), 2019.

89. Z. Wang, D. Acuna, H. Ling, A. Kar, S. Fidler. Object Instance Annotation with Deep Extreme
Level Set Evolution. In Computer Vision and Pattern Recognition (CVPR), 2019.
90. Y.-H. Liao, X. Puig, M. Boben, A. Torralba, S. Fidler. Synthesizing Environment-Aware Activities
via Activity Sketches. In Computer Vision and Pattern Recognition (CVPR), 2019.
91. M. Shugrina, Z. Liang, A. Kar, J. Li, A. Singh, K. Singh, S. Fidler. Creative Flow+ Dataset. In
Computer Vision and Pattern Recognition (CVPR), 2019.
92. D. Cheng, R. Liao, S. Fidler, R. Urtasun. DARNet: Deep Active Ray Network for Building
Segmentation. In Computer Vision and Pattern Recognition (CVPR), 2019.
93. D. Moltisanti, S. Fidler, D. Damen. Action Recognition from Single Timestamp Supervision in
Untrimmed Videos. In Computer Vision and Pattern Recognition (CVPR), 2019.
94. C. Wang, R. Grosse, S. Fidler, G. Zhang. EigenDamage: Structured Pruning in the KroneckerFactored Eigenbasis. In International Conference on Machine Learning (ICML), 2019.
95. T. Wang, Y. Zhou, S. Fidler, J. Ba. Neural Graph Evolution: Automatic Robot Design. In
International Conference on Learning Representations (ICLR), 2019.
96. S. W. Kim, M. Tapaswi, S. Fidler. Visual Reasoning by Progressive Module Networks. In International Conference on Learning Representations (ICLR), 2019.
97. M. Shugrina, W. Zhang, F. Chevalier, S. Fidler, K. Singh. Color Builder: A Direct Manipulation
Interface for Versatile Color Theme Authoring. In Conference on Human Factors in Computing
Systems (CHI), 2019.
98. B. Dai, S. Fidler, D. Lin. A Neural Compositional Paradigm for Image Captioning. In Neural
Information Processing Systems (NeurIPS), 2018.
99. E. Corona, K. Kundu, S. Fidler. Pose Estimation for Objects with Rotational Symmetry. In
International Conference on Intelligent Robots (IROS), 2018.
100. D. Damen, H. Doughty, G. M. Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J.
Munro, T. Perrett, W. Price, M. Wray Scaling Egocentric Vision: The EPIC-KITCHENS Datasets.
In European Conference on Computer Vision (ECCV), 2018, oral presentation.
101. F. Faghri, J. Kiros, D. Fleet, S. Fidler. VSE++: Improving Visual-Semantic Embeddings with
Hard Negatives. In British Machine Vision Conference (BMVC), 2018, spotlight presentation.
102. X. Puig, K. Ra, M. Boben, J. Li, T. Wang, S. Fidler, A. Torralba. VirtualHome: Simulating
Household Activities via Programs. In Computer Vision and Pattern Recognition (CVPR), 2018,
oral presentation.
103. P. Vicol, M. Tapaswi, L. Castrejon, S. Fidler. MovieGraphs: Towards Understanding HumanCentric Situations from Videos. In Computer Vision and Pattern Recognition (CVPR), 2018, spotlight presentation.
104. Y. Zhou, M. Tapaswi, S. Fidler. Now You Shake Me: Towards Automatic 4D Cinema. In Computer
Vision and Pattern Recognition (CVPR), 2018, spotlight presentation.
105. D. Acuna, H. Ling, A. Kar, S. Fidler. Efficient Interactive Annotation of Segmentation Datasetswith Polygon-RNN++. In Computer Vision and Pattern Recognition (CVPR), 2018.
106. C. -Y. Chuang, J. Li, A. Torralba, S. Fidler. Learning to Act Properly: Predicting and Explaining
Affordances from Images. In Computer Vision and Pattern Recognition (CVPR), 2018.
107. H. Chu, S. Fidler. A Face to Face Neural Conversation Model. In Computer Vision and Pattern
Recognition (CVPR), 2018.

108. H. Chu, W. -C. Ma, K. Kundu, R. Urtasun, S. Fidler. SurfConv: Bridging 3D and 2D Convolution
for RGBD Images. In Computer Vision and Pattern Recognition (CVPR), 2018.
109. T. Wang, R. Liao, J. Ba, S. Fidler. NerveNet: Learning Structured Policy with Graph Neural
Networks. In International Conference on Learning Representations (ICLR), 2018.
110. H. Ling, S. Fidler. Teaching Machines to Describe Images via Natural Language Feedback. In
Neural Information Processing Systems (NIPS), 2017.
111. B. Dai, S. Fidler, R. Urtasun, D. Lin. Towards Diverse and Natural Image Descriptions via a Conditional GAN. In International Conference on Computer Vision (ICCV), 2017, oral presentation.
112. X. Qi, R. Liao, J. Jia, S. Fidler, R. Urtasun. 3D Graph Neural Networks for RGBD Semantic
Segmentation. In International Conference on Computer Vision (ICCV), 2017, oral presentation.
113. S. Wang, M. Bai, G. Mattyus, H. Chu, W. Luo, B. Yang, J. Liang, J. Cheverie, S. Fidler, R. Urtasun.
TorontoCity: Seeing the World with a Million Eyes. In International Conference on Computer Vision(ICCV), 2017, spotlight presentation.
114. R. Li, M. Tapaswi, R. Liao, J. Jia, R. Urtasun, S. Fidler. Situation Recognition with Graph Neural
Networks. In International Conference on Computer Vision (ICCV), 2017115. H. Zhao, X. Puig, B. Zhou, S. Fidler, A. Torralba. Open Vocabulary Scene Parsing. In International
Conference on Computer Vision (ICCV), 2017116. S. Zhu, S. Fidler, R. Urtasun, D. Lin, C.C. Loy. Be Your Own Prada: Fashion Synthesis with
Structural Coherence. In International Conference on Computer Vision (ICCV), 2017117. S. Liu, J. Jia, S. Fidler, Raquel Urtasun. Sequential Grouping Networks for Instance Segmentation.
In International Conference on Computer Vision (ICCV), 2017118. L. Castrejon, K. Kundu, R. Urtasun, S. Fidler. Annotating Object Instances with a Polygon-RNN.
In Computer Vision and Pattern Recognition (CVPR), 2017, best paper honorable mention.
119. N. Homayounfar, S. Fidler, R. Urtasun. Sports Field Localization via Deep Structured Models. In
Computer Vision and Pattern Recognition (CVPR), 2017.
120. B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso and A. Torralba. Scene Parsing through ADE20K
Dataset. In Computer Vision and Pattern Recognition (CVPR), 2017.
121. W.-C. Ma, S. Wang, M. A. Brubaker, S. Fidler, R. Urtasun. Find your way by observing the sun
and other semantic cues. In International Conference on Robotics and Automation (ICRA), 2017.
122. S. Wang, S. Fidler, R. Urtasun. Proximal Deep Structured Models. In Neural Information Processing Systems (NIPS), 2016.
123. H. Chu, S. Wang, R. Urtasun, S. Fidler. HouseCraft: Building Houses from Rental Ads and Street
Views. In European Conference on Computer Vision (ECCV), 2016.
124. M. Tapaswi, Y. Zhu, R. Stiefelhagen, A. Torralba, R. Urtasun, S. Fidler. MovieQA: Understanding Stories in Movies through Question-Answering. In Computer Vision and Pattern Recognition(CVPR), 2016, spotlight presentation.
125. Z. Zhang, S. Fidler, R. Urtasun. Instance-Level Segmentation for Autonomous Driving with Deep
Densely Connected MRFs. In Computer Vision and Pattern Recognition (CVPR), 2016.
126. X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, R. Urtasun. Monocular 3D Object Detection forAutonomous Driving. In Computer Vision and Pattern Recognition (CVPR), 2016.

127. G. Mattyus, S. Wang, S. Fidler, R. Urtasun. HD Maps: Fine-grained Road Segmentation by
Parsing Ground and Aerial Images. In Computer Vision and Pattern Recognition (CVPR), 2016.
128. I. Vendrov, R. Kiros, S. Fidler, R. Urtasun. Order-Embeddings of Images and Language. In
International Conference on Learning Representations (ICLR), 2016, oral presentation.
129. Y. Zhu, R. Kiros, R. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba, S. Fidler. Aligning Books
and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books. In
International Conference on Computer Vision (ICCV), 2015, oral presentation.
130. S. Wang, S. Fidler, R. Urtasun. Lost Shopping! Monocular Localization in Large Indoor Spaces.
In International Conference on Computer Vision (ICCV), 2015, oral presentation.
131. J. Ba, K. Swersky, S. Fidler, R. Salakhutdinov. Predicting Deep Zero-Shot Convolutional Neural
Networks using Textual Descriptions. In International Conference on Computer Vision (ICCV),2015.
132. Z. Zhang, A. Schwing, S. Fidler, R. Urtasun. Monocular Object Instance Segmentation and Depth
Ordering with CNNs. In International Conference on Computer Vision (ICCV), 2015.
133. G. Matthyus, S. Wang, S. Fidler, Raquel Urtasun. Enhancing World Maps by Parsing Aerial
Images. In International Conference on Computer Vision (ICCV), 2015.
134. T. Lee, S. Fidler, S. Dickinson. A Learning Framework for Generating Region Proposals with
Mid-level Cues. In International Conference on Computer Vision (ICCV), 2015.
135. R. Kiros, Y. Zhu, R. Salakhutdinov, R. Zemel, A. Torralba, R. Urtasun, S. Fidler. Skip-Thought
Vectors. Neural Information Processing Systems (NIPS), 2015.
136. X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler, R. Urtasun. 3D Object Proposals
for Accurate Object Class Detection. Neural Information Processing Systems (NIPS), 2015.
137. D. Lin, C. Kong, S. Fidler, R. Urtasun. Generating Multi-Sentence Lingual Descriptions of Indoor
Scenes. In In British Machine Vision Conference (BMVC), 2015, oral presentation.
138. C. Liu, A. Schwing, K. Kundu, R. Urtasun, S. Fidler. Rent3D: Floor-Plan Priors for Monocular
Layout Estimation. In Computer Vision and Pattern Recognition (CVPR), 2015, oral presentation.
139. S. Wang, S. Fidler, R. Urtasun. Holistic 3D Scene Understanding from a Single Geo-tagged Image.
In Computer Vision and Pattern Recognition (CVPR), 2015, oral presentation.
140. Y. Zhu, R. Urtasun, R. Salakhutdinov, S. Fidler. segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection. In Computer Vision and Pattern Recognition(CVPR), 2015.
141. E. Simo-Serra, S. Fidler, F. Moreno-Noguer, R. Urtasun. Neuroaesthetics in Fashion: Modeling thePerception of Beauty. In Computer Vision and Pattern Recognition (CVPR), 2015.
142. J. Yao, M. Boben, S. Fidler, R. Urtasun. Real-Time Coarse-to-fine Topologically Preserving Segmentation. In Computer Vision and Pattern Recognition (CVPR), 2015.
143. E. Simo-Serra, S. Fidler, F. Moreno-Noguer, R. Urtasun. A High Performance CRF Model for
Clothes Parsing. In Asian Conference on Computer Vision (ACCV), 2014.
144. T. Lee, S. Fidler, Sven Dickinson. Multi-cue Mid-level Grouping. In Asian Conference on Computer
Vision (ACCV), 2014.
145. C. Kong, D. Lin, M. Bansal, R. Urtasun, S. Fidler. What are you talking about? Text-to-Image
Coreference. In Conference on Computer Vision and Pattern Recognition (CVPR), 2014.

146. D. Lin, S. Fidler, C. Kong, R. Urtasun. Visual Semantic Search: Retrieving Videos via Complex
Textual Queries. In Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
147. L.-C. Chen, S. Fidler, A. Yuille, R. Urtasun. Beat the MTurkers: Automatic Image Labeling from
Weak 3D Supervision. In Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
148. R. Mottaghi, X. Chen, X. Liu, S. Fidler, R. Urtasun, A. Yuille. The Role of Context for Object
Detection and Semantic Segmentation in the Wild. In Conference on Computer Vision and Pattern
Recognition (CVPR), 2014.
149. X. Chen, R. Mottaghi, X. Liu, N.-G. Cho, S. Fidler, R. Urtasun, A. Yuille. Detect What You
Can: Detecting and Representing Objects using Holistic Models and Body Parts. In Conference on
Computer Vision and Pattern Recognition (CVPR), 2014.
150. D. Lin, S. Fidler, R. Urtasun. Holistic Scene Understanding for 3D Object Detection with RGBD
cameras. In International Conference on Computer Vision (ICCV), 2013, oral presentation.
151. A. Schwing, S. Fidler, M. Pollefeys, R. Urtasun. Box In the Box: Joint 3D Layout and Object
Reasoning from Single Images. In International Conference on Computer Vision (ICCV), 2013.
152. T. Lee, S. Fidler, S. Dickinson. Detecting Curved Symmetric Parts using a Deformable Disc Model.
In International Conference on Computer Vision (ICCV), 2013.
153. R. Mottaghi, S. Fidler, J. Yao, R. Urtasun, D. Parikh. Analyzing Semantic Segmentation Using
Human-Machine Hybrid CRFs. In IEEE Computer Vision and Pattern Recognition (CVPR), 2013.
154. S. Fidler, R. Mottaghi, A. Yuille, R. Urtasun. Bottom-up Segmentation for Top-down Detection.
In IEEE Computer Vision and Pattern Recognition (CVPR), 2013.
155. S. Fidler, A. Sharma, R. Urtasun. A Sentence is Worth a Thousand Pixels. In IEEE Computer
Vision and Pattern Recognition (CVPR), 2013.
156. S. Fidler, S. Dickinson, R. Urtasun. 3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model. In Neural Information Processing Systems Conference (NIPS), 2012,
spotlight presentation.
157. J. Yao, S. Fidler, R. Urtasun. Describing the Scene as a Whole: Joint Object Detection, Scene Classification and Semantic Segmentation. In IEEE Computer Vision and Pattern Recognition (CVPR),
2012.
158. Z. Zhang, S. Fidler, J. W. Waggoner, Y. Cao, J. M. Siskind, S. Dickinson, W. Wang. Super-edgegrouping for object localization by combining appearance and shape information. In IEEE Computer
Vision and Pattern Recognition (CVPR), 2012.
159. A. Barbu, A. Bridge, Z. Burchill, D. Coroian, S. Dickinson, S. Fidler, A. Michaux, S. Mussman,
S. Narayanaswamy, D. Salvi, L. Schmidt, J. Shangguan, J. Siskind, J. Waggoner, S. Wang, J. Wei,
Y. Yin, and Z. Zhang. Video In Sentences Out. Conference on Uncertainty in Artificial Intelligence(UAI), 2012, oral presentation.
160. W. May, S. Fidler, A. Fazly, S. Stevenson, and S. Dickinson. Unsupervised Disambiguation of
Image Captions. First Joint Conference on Lexical and Computational Semantics (*SEM), 2012.
161. T. Lee, S. Fidler, A. Levinshtein, and S. Dickinson. Learning Categorical Shape from Captioned
Images. Canadian Conference on Computer and Robot Vision (CRV), 2012.
162. S. Karayev, M. Fritz, S. Fidler, T. Darrell. A Probabilistic Model for Recursive Factorized Image
Features. In IEEE Computer Vision and Pattern Recognition (CVPR), 2011.

163. S. Fidler, M. Boben, A. Leonardis. A coarse-to-fine Taxonomy of Constellations for Fast Multi-class
Object Detection. In European Conference on Computer Vision (ECCV), 2010.
164. S. Fidler, M. Boben, A. Leonardis. Evaluating multi-class learning strategies in a generative hierarchical framework for object detection. In Neural Information Processing Systems Conference(NIPS), 2009.
165. S. Fidler, M. Boben, A. Leonardis. Optimization framework for learning a hierarchical shape
vocabulary for object class detection. In British Machine Vision Conference (BMVC), 2009.
166. S. Fidler, M. Boben, A. Leonardis. Similarity-based cross-layered hierarchical representation for
object categorization. In IEEE Computer Vision and Pattern Recognition (CVPR), 2008.
167. S. Fidler, A. Leonardis. Towards scalable representations of object categories : learning a hierarchyof parts. In IEEE Computer Vision and Pattern Recognition (CVPR), 2007.
168. A. Leonardis, S. Fidler. Learning hierarchical representations of object categories for robot vision.
In ISRR 2007 : 13th International Symposium of Robotics Research, 2007, Hiroshima, Japan, pp.
125 − 136. Invited paper.
169. S. Fidler, G. Berginc, A. Leonardis. Hierarchical Statistical Learning of Generic Parts of Object
Structure. In IEEE Computer Vision and Pattern Recognition (CVPR), 2006.
170. D. Skočaj, A. Leonardis, S. Fidler. Robust estimation of canonical correlation coefficients. In
Digital imaging in media and education : 28th workshop of the Austrian Association for Pattern
Recognition (AAPR), 2004, pp. 15-22.
171. S. Fidler, A. Leonardis. Robust LDA classification by subsampling. In Workshop in Statistical
Analysis in Computer Vision in conjunction with IEEE Computer Vision and Pattern Recognition,
2003.
172. S. Fidler, A. Leonardis. Robust LDA classification. In Vision in a dynamic world: 27th workshopof the Austrian Association for Pattern Recognition (AAPR), 2003, pp. 119-126. Best paper award.

Theses
PhD thesis
Recognizing Visual Object categories with Subspace Methods and a Learned Hierarchical Shape
Vocabulary. University of Ljubljana, 2010.
Diploma thesis
Independent Component Analysis. University of Ljubljana, 2002.