Concept

Conditional random field

Related publications (207)

GestureGAN for Hand Gesture-to-Gesture Translation in the Wild

Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gestu ...

ASSOC COMPUTING MACHINERY2018

Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV serie ...

ASSOC COMPUTING MACHINERY2018

Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

ACM2018

Ocular Structures Segmentation from Multi-sequences MRI Using 3D Unet with Fully Connected CRFs

Meritxell Bach Cuadra, Raphael Sznitman, Alessia Pica

The use of 3D Magnetic Resonance Imaging (MRI) has attracted growing attention for the purpose of diagnosis and treatment planning of intraocular ocular cancers. Precise segmentation of such tumors are highly important to characterize tumors, their progres ...

SPRINGER INTERNATIONAL PUBLISHING AG2018

Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET

Olivier Sauter, Ambrogio Fasoli, Basil Duval, Stefano Coda, Jonathan Graves, Yves Martin, Duccio Testa, Patrick Blanchard, Alessandro Pau, Cristian Sommariva, Henri Weisen, Richard Pitts, Yann Camenen, Jan Horacek, Javier García Hernández, Marco Wischmeier, Nicola Vianello, Mikhail Maslov, Federico Nespoli, Yao Zhou, David Pfefferlé, Davide Galassi, Antonio José Pereira de Figueiredo, Jonathan Marc Philippe Faustin, Liang Yao, Dalziel Joseph Wilson, Hamish William Patten, Samuel Lanthaler, Bernhard Sieglin, Otto Asunta

Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not ...

IOP PUBLISHING LTD2018

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...

Ieee2017

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Ieee2017

Efficient Linear Programming for Dense CRFs

Mathieu Salzmann

The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxat ...

2017

Indoor Scene Parsing with Instance Segmentation, Semantic Labeling and Support Relationship Inference

Mathieu Salzmann, Wei Zhuo

Over the years, indoor scene parsing has attracted a growing interest in the computer vision community. Existing methods have typically focused on diverse subtasks of this challenging problem. In particular, while some of them aim at segmenting the image i ...

Ieee2017

ChoiceRank: Identifying Preferences from Node Traffic in Networks

Matthias Grossglauser, Lucas Maystre

Understanding how users navigate in a network is of high interest in many applications. We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities. We cast it as a preference lear ...

2017

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