Search Results for author: Julieta Martinez

Found 9 papers, 6 papers with code

A simple yet effective baseline for 3d human pose estimation

14 code implementations ICCV 2017 Julieta Martinez, Rayat Hossain, Javier Romero, James J. Little

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 given raw image pixels.

3D Pose Estimation Monocular 3D Human Pose Estimation

On human motion prediction using recurrent neural networks

8 code implementations CVPR 2017 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 motion prediction for virtual and augmented reality.

Human motion prediction Human Pose Forecasting +3

Stacked Quantizers for Compositional Vector Compression

2 code implementations8 Nov 2014 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 vectors into small binary codes.

Quantization

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

1 code implementation ECCV 2018 Julieta Martinez, Shobhit Zakhmi, Holger H. Hoos, James 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.

Quantization

3D Pose from Motion for Cross-view Action Recognition via Non-linear Circulant Temporal Encoding

1 code implementation CVPR 2014 Ankur Gupta, Julieta Martinez, James J. Little, Robert J. Woodham

We describe a new approach to transfer knowledge across views for action recognition by using examples from a large collection of unlabelled mocap data.

Action Recognition Temporal Action Localization

Learning to Localize Through Compressed Binary Maps

no code implementations CVPR 2019 Xinkai Wei, Ioan Andrei Bârsan, Shenlong Wang, Julieta Martinez, Raquel Urtasun

One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps.

Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars

no code implementations23 Dec 2020 Julieta Martinez, Sasha Doubov, Jack Fan, Ioan Andrei Bârsan, Shenlong Wang, Gellért Máttyus, Raquel Urtasun

We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles.

LIDAR Semantic Segmentation Retrieval +2

Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

no code implementations CVPR 2021 John Phillips, Julieta Martinez, Ioan Andrei Bârsan, Sergio Casas, Abbas Sadat, Raquel Urtasun

Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning.

Motion Forecasting Motion Planning +1

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