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Social NCE: Contrastive Learning of Socially-aware Motion Representations

Alexandre Massoud Alahi, Yuejiang Liu

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

2021

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Public funding agencies aim to fund novel breakthrough research to promote the radical scientific discoveries of tomorrow. Identifying the profiles of scientists being financed to pursue their research is therefore crucial. This paper shows that the fundin ...

OXFORD UNIV PRESS2021

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Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on di ...

2020

Vision based pixel-level bridge structural damage detection using a link ASPP network

Wenlong Deng

Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface s ...

2020

Learning stereo reconstruction with deep neural networks

Stepan Tulyakov

Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these ...

EPFL2020

Learning to Reduce Annotation Load

Ksenia Konyushkova

Modern machine learning methods and their applications in computer vision are known to crave for large amounts of training data to reach their full potential. Because training data is mostly obtained through humans who manually label samples, it induces a ...

EPFL2019

Efficiently Learning Fourier Sparse Set Functions

Andreas Krause, Mikhail Kapralov, Amir Zandieh

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

Jean-Philippe Thiran, Meritxell Bach Cuadra, Tobias Kober, Cristina Granziera, Jonas Richiardi, Mario Joao Fartaria de Oliveira, Francesco La Rosa

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease pat ...

Springer2018

Deep Learning Works in Practice. But Does it Work in Theory?

Rachid Guerraoui, Le Nguyen Hoang

Deep learning relies on a very specific kind of neural networks: those superposing several neural layers. In the last few years, deep learning achieved major breakthroughs in many tasks such as image analysis, speech recognition, natural language processin ...

2018