Hi! I'm Julieta

Since 2018 I have been at Uber ATG Toronto, working with Raquel Urtasun.

In the fall of 2016 I visited Michael Black and Javier Romero in the Perceiving Systems group at MPI Tuebingen.

From 2015 to 2018 I was a PhD student in the Department of Computer Science at the University of British Columbia, supervised by Jim Little and Holger Hoos.

Before that I was in Mexico.

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Selected papers

Compressed localization

Xinkai Wei, Ioan Andrei Bârsan, Julieta Martinez, Shenlong Wang, Raquel Urtasun
Learning to localize through compressed binary maps In CVPR 2019 (25.2% acceptance rate).
We introduce a localization system based on LiDAR intensity maps that is able to localize with centrimetre-level accuracy against highly compressed representations.

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Human motion prediction

Julieta Martinez, Michael J. Black, Javier Romero.
On human motion prediction using recurrent neural networks. In CVPR 2017 (29.84% acceptance rate)
We take a close look at deep recurrent approaches for human motion prediction, and propose a simple and scalable architecture that outperforms the state of the art.

Faster and more accurate multi-codebook quantization

Julieta Martinez, Shobhit Zakhmi, Holger H. Hoos and James J. Little.
LSQ++: lower running time and higher recall in multi-codebook quantization. In ECCV 2018 (29.4% acceptance rate).
We benchmark multi-codebook quantization (MCQ) approaches on an equal footing and propose two improvements that make MCQ faster and more accurate.

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Revisiting additive quantization

Julieta Martinez, Joris Clement, Holger H. Hoos, James J. Little.
Revisiting additive quantization. In ECCV 2016 (26.6% acceptance rate)
Additive quantization (AQ) is a promising vector compression approach for large-scale approximate nearest neighbour search. We introduce an optimization method for AQ that pushes it beyond the state of the art.

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3d pose baseline

Julieta Martinez, Rayat Hossain, Javier Romero and James J. Little.
A simple yet effective baseline for 3d human pose estimation. In ICCV 2017 (28.9% acceptance rate).
We propose a simple deep learning baseline for 3d human pose estimation that outperforms the state of the art.

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3d pose from motion

Ankur Gupta*, Julieta Martinez*, James J. Little, Robert J. Woodham.
3D pose from motion for cross-view action recognition. In CVPR 2014 (29.88% acceptance rate)
An approach to improving cross-view action recognition by retrieving mocap given video sequences.

Other papers

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Mocap retrieval

Ankur Gupta, John He, Julieta Martinez, James J. Little and Robert J. Woodham.
Efficient video-based retrieval of human motion with flexible alignment. In WACV 2016
We formalize the problem of video-based mocap retrieval. We also investigate different retrieval methods for this task.

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Stacked quantizers

Julieta Martinez, Holger H. Hoos and James J. Little. Stacked quantizers for compositional vector compression. In arxiv (2014)
Some of my early attempts to improve multi-codebook quantization. This approach is equivalent to enhanced RVQ, and has been superceeded by our work on revisiting AQ. The code is very accessible though!

Yet more papers

Solving multi-codebook quantization in the GPU | .bib

Julieta Martinez, Holger H. Hoos and James J. Little. In 4th Workshop on Web-scale Vision and Social Media (VSM), at ECCV 2016.
Complement to our work on Revisiting AQ. Details our GPU implementation.

Hash bank | .bib

Frederick Tung, Julieta Martinez, Holger H. Hoos and James J. Little. In WACV 2015.
A vector is mapped to one of many hash functions, which improves accuracy at increased query time.

BO on FLANN | .bib

Julieta Martinez, James J. Little and Nando de Freitas. In WACV 2014.
We showed that Bayesian optimization with Gaussian processes would be a great addition to FLANN.


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My information visualization final project, taught by the wonderful Tamara Munzner. A javascript visualization tool for image retrieval (2015).

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Efros and Leung JS

A javascript implementation of a classic method for texture synthesis (2015).

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Pavlov is no simpleton

I tried to reproduce the results of a 1993 paper on evolutionary dynamics by Sigmund and Novak. I also wrote a blog post about it (2014).


I am/have served as a reviewer for CVIU, ICRA 16, CVPR 18, ECCV 18, IROS 18, NIPS 18.

I have done science outreach with GIRLsmarts4tech, UBC women in science and @realscientists.

On the fall of 2014 I started organizing CVRG, the Computer Vision Reading Group at UBC.

On the summer of 2013 I made a chrome extension that lets you share pdfs on Pinterest. Take a look at my research board!