Oisin Mac Aodha

My name is Oisin Mac Aodha (how to pronounce my name) and I am a Lecturer (aka Assistant Professor) in Machine Learning in the School of Informatics at the University of Edinburgh. My current research interests are in the areas of computer vision and machine learning, with a specific emphasis on human-in-the-loop methods such as machine teaching.

From 2016-2019 I was fortunate to be a postdoc in Prof. Pietro Perona's Computational Vision Lab at Caltech. Previous to Caltech, I spent three great years (2013-2016) as a postdoc in the Department of Computer Science at the University College of London with Prof. Gabriel Brostow and Prof. Kate Jones. There I worked on interactive machine learning, where our goal was to design algorithms to enable non-programming scientists to semi-automatically explore events of interest in vast quantities of audio and visual data.

I did both my MSc (with Dr. Simon Prince) and PhD (with Prof. Gabriel Brostow) at UCL and have an undergraduate degree in electronic engineering from NUI Galway in Ireland. Before my PhD, I was a research assistant for one year in Prof. Marc Pollefeys' group at ETH Zurich.

Email: oisin.macaodha (at) ed.ac.uk
Twitter: @oisinmacaodha
Office: Informatics Forum 2.24A

News

Jul 2020: Presenting at the AI for Social Good workshop. Video of the talk is here.
Jul 2020: Giving a talk at the Optimizing Human Learning workshop. Video of the talk is here.
Jun 2020: You can find the videos of the talks from our FGVC7 workshop here.
May 2020: I'll be an Area Chair for NeurIPS 2020.
Mar 2020: Serving as a Program Chair for BMVC 2020. Consider submitting!
Dec 2019: Co-organizing FGVC7 workshop at CVPR 2020.
Nov 2019: Vacancies: Several openings for PhD students - see here for more details.
Oct 2019: Just started as a Lecturer (aka Assistant Professor) in Machine Learning at the University of Edinburgh.
Jul 2019: Serving on the Program Committee for Workshop on Computer Vision for Wildlife Conservation at ICCV 2019. Consider submitting your papers!
Jun 2019: Serving as an Area Chair for WACV 2020.
May 2019: Code released for our Monodepth2 paper.
Apr 2019: FGVC6 competitions now live on Kaggle.
Jan 2019: Co-organizing FGVC6 workshop at CVPR 2019.
Sep 2018: Serving as an Area Chair for ACCV 2018.
Jul 2018: Check out Niantic's occlusion demo using our monocular depth work.
Jun 2018: FGVC5 held at CVPR 2018.
Feb 2018: Launched iNaturalist 2018 challenge.
Jan 2018: Gave a talk to LA school children about the importance of bats.
Jan 2018: Co-organizing FGVC5 workshop at CVPR 2018.
Aug 2017: Read about the iNaturalist Challenge 2017 that we ran in conjunction with Caltech, iNaturalist, Cornell, Google, and Kaggle.
Jul 2017: Helped organize the Fourth Fine-Grained Visual Categorization workshop at CPVR this summer.
Jul 2017: Read about our monocular depth estimation project at UCL and The Engineer.
Jun 2017: Live deployment of our bat detection system in the former Olympic Park in London. Read about it on the BBC or follow our twitter page to get daily updates.
Sep 2016: Interviewed by SVT for our work on using machine learning to detect bats.
Aug 2016: Read about our handwriting synthesis project at the BBC, UCL, Engadget, Reuters, Gizmodo, and The Irish Times.
Jul 2016: Moved to Caltech to do a postdoc.
Mar 2016: Gave a talk about Bat Detective at UCL's Grant Museum.
Apr 2015: Blog about bat call detection over at Methods in Ecology and Evolution.

Publications

More details on Google Scholar.

Learning Stereo from Single Images
Jamie Watson, Oisin Mac Aodha, Daniyar Turmukhambetov, Gabriel Brostow, and Michael Firman
ECCV 2020 (Oral)
[paper] [code]

Geocoding of Trees From Street Addresses and Street-Level Images
Daniel Laumer, Nico Lang, Natalie van Doorn, Oisin Mac Aodha, Pietro Perona, and Jan Dirk Wegner
Journal of Photogrammetry and Remote Sensing 2020
[paper] [arxiv]

Teaching Multiple Concepts to a Forgetful Learner
Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, and Adish Singla
NeurIPS 2019
[paper]

Presence-Only Geographical Priors for Fine-Grained Image Classification
Oisin Mac Aodha, Elijah Cole, and Pietro Perona
ICCV 2019
[paper] [code] [video] [webpage]

Digging Into Self-Supervised Monocular Depth Estimation
Clement Godard, Oisin Mac Aodha, Michael Firman, and Gabriel Brostow
ICCV 2019
[paper] [video] [code]

Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, and Yisong Yue
NeurIPS 2018
[paper]

It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data
Matteo Ruggero Ronchi, Oisin Mac Aodha, Robert Eng, and Pietro Perona
BMVC 2018 (Oral)
[paper] [webpage] [code] [video]

Teaching Categories to Human Learners with Visual Explanations
Oisin Mac Aodha, Shihan Su, Yuxin Chen, Pietro Perona, and Yisong Yue
CVPR 2018 (Spotlight)
[paper] [code] [video]

Context Embedding Networks
Kun ho Kim, Oisin Mac Aodha, and Pietro Perona
CVPR 2018 (Spotlight)
[paper]

The iNaturalist Species Classification and Detection Dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie
CVPR 2018 (Spotlight)
[paper] [webpage] [video]

Bat Detective - Deep Learning Tools for Bat Acoustic Signal Detection
Oisin Mac Aodha, Rory Gibb, Kate Barlow, Ella Browning, Michael Firman, Robin Freeman, Briana Harder, Libby Kinsey, Gary Mead, Stuart Newson, Ivan Pandourski, Stuart Parsons, Jon Russ, Abigel Szodoray-Paradi, Farkas Szodoray-Paradi, Elena Tilova, Mark Girolami, Gabriel J. Brostow, and Kate E. Jones
PLOS Computational Biology 2018 [paper] [webpage]

Near-Optimal Machine Teaching via Explanatory Teaching Sets
Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, and Yisong Yue
AISTATS 2018
[paper]

Interpretable Machine Teaching via Feature Feedback
Shihan Su, Yuxin Chen, Oisin Mac Aodha, Pietro Perona, and Yisong Yue
NeurIPS Workshop Teaching Machines, Robots, and Humans 2017
[paper]

Unsupervised Monocular Depth Estimation with Left-Right Consistency
Clement Godard, Oisin Mac Aodha, and Gabriel J. Brostow
CVPR 2017 (Oral)
[paper] [code] [webpage]

Structured Prediction of Unobserved Voxels From a Single Depth Image
Michael Firman, Oisin Mac Aodha, Simon Julier, and Gabriel J. Brostow
CVPR 2016 (Oral)
[paper] [webpage]

My text in Your Handwriting
Tom S.F. Haines, Oisin Mac Aodha, and Gabriel J. Brostow
Transactions on Graphics (SIGGRAPH) 2016
[paper] [code] [webpage]

Becoming the Expert - Interactive Multi-Class Machine Teaching
Edward Johns, Oisin Mac Aodha, and Gabriel J. Brostow
CVPR 2015 (Oral)
[paper] [webpage]

Putting the Scientist in the Loop - Accelerating Scientific Progress with Interactive Machine Learning
Oisin Mac Aodha, Vassilios Stathopoulos, Michael Terry, Kate E. Jones, Gabriel J. Brostow, and Mark Girolami
ICPR 2014
[paper]

Hierarchical Subquery Evaluation for Active Learning on a Graph
Oisin Mac Aodha, Neill D.F. Campbell, Jan Kautz, and Gabriel J. Brostow
CVPR 2014 (Oral)
[paper] [webpage]

Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees
Oisin Mac Aodha and Gabriel J. Brostow
ICCV 2013
[paper] [webpage]

Learning a Confidence Measure for Optical Flow
Oisin Mac Aodha, Ahmad Humayun, Marc Pollefeys, and Gabriel J. Brostow
PAMI 2013
[paper] [webpage]

Patch Based Synthesis for Single Depth Image Super-Resolution
Oisin Mac Aodha, Neill D.F. Campbell, Arun Nair, and Gabriel J. Brostow
ECCV 2012
[paper] [webpage]

Learning to Find Occlusion Regions
Ahmad Humayun, Oisin Mac Aodha, and Gabriel J. Brostow
CVPR 2011
[paper] [webpage]

Segmenting Video Into Classes of Algorithm-Suitability
Oisin Mac Aodha, Gabriel J Brostow, and Marc Pollefeys
CVPR 2010
[paper] [webpage]

Evolving Plastic Responses in Artificial Cell Models
John Maher, Fearghal Morgan, and Oisin Mac Aodha
Congress on Evolutionary Computation 2009
[paper]

Theses

Supervised Algorithm Selection for Flow and Other Computer Vision Problems
PhD Thesis
UCL 2014
[thesis]

Reconstruction and Recognition of 3D Face Models
MSc Thesis
UCL 2008
[thesis]

Teaching

Dec 2015 UCL: Introduction to Machine Learning for Ecologists