Before that I was in Mexico.
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.
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.
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.
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.
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.
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.
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!
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.
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.
I am/have served as a reviewer for CVIU, ICRA 16, CVPR 18, ECCV 18, IROS 18, NIPS 18.
On the fall of 2014 I started organizing CVRG, the Computer Vision Reading Group at UBC.