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Julieta Martinez

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0 BLOG ARTICLES 2 RESEARCH PAPERS
Julieta is a researcher at Uber ATG Toronto, focused on computer vision for autonomous driving. Julieta obtained a MSc and a PhD at the University of British Columbia.

Research Papers

Learning to Localize through Compressed Binary Maps

X. Wei, I. A. Bârsan, S. Wang, J. Martinez, R. Urtasun
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps. In this paper we propose to learn to compress the map representation such that it is optimal for the localization task. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019

LSQ++: lower running time and higher recall in multi-codebook quantization

J. Martinez, S. Zakhmi, H. Hoos, and J. Little
Multi-codebook quantization (MCQ) is the task of expressing a set of vectors as accurately as possible in terms of discrete entries in multiple bases. Work in MCQ is heavily focused on lowering quantization error, thereby improving distance estimation and recall on benchmarks of visual descriptors at a fixed memory budget. [...] [PDF]
European Conference on Computer Vision (ECCV), 2018

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