95 citations · 141 across the 6 of their papers we have counts for
7 papers
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
John Phillips, Julieta Martinez, Ioan Andrei Bârsan +3
Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning. However, these…
Learning to Localize Through Compressed Binary Maps
Xinkai Wei, Ioan Andrei Bârsan, Shenlong Wang +2
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 compr…
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
Julieta Martinez, Sasha Doubov, Jack Fan +4
We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M,…
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Julieta Martinez, Jashan Shewakramani, Ting Wei Liu +3
Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. In this context, vector quantization is an appealing fr…
On human motion prediction using recurrent neural networks
Julieta Martinez, Michael J. Black, Javier Romero
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and moti…
A simple yet effective baseline for 3d human pose estimation
Julieta Martinez, Rayat Hossain, Javier Romero +1
Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations…
Stacked Quantizers for Compositional Vector Compression
Julieta Martinez, Holger H. Hoos, James J. Little
Recently, Babenko and Lempitsky introduced Additive Quantization (AQ), a generalization of Product Quantization (PQ) where a non-independent set of codebooks is used to compress ve…