Publications related to Monte Carlo Video Text Segmentation

How to Boost Face Recognition with StyleGAN?

State-of-the-art face recognition systems require vast amounts of labeled training data. Given the priority of privacy in face recognition applications, the data is limited to celebrity web crawls, which have issues such as limited numbers of identities. O ...

Ieee Computer Soc2023

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

2018

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

EPFL2017

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

Sciences et Techniques de l’Ingénieur (STI)2017

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Matthias Grossglauser, Lucas Maystre

We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly. If all comparison outcomes are consistent with the ranking, the optimal sol ...

2017

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Touradj Ebrahimi, Lin Yuan, Ashutosh Singla

Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and convolutional ...

Assoc Computing Machinery2016

Compressive Spectral Clustering

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a fea ...

2016

Monte Carlo Video Text Segmentation

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How to Boost Face Recognition with StyleGAN?

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

Large-Scale Image Segmentation with Convolutional Networks

Large-Scale Image Segmentation with Convolutional Networks

Safe Adaptive Importance Sampling

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Shapes from Pixels

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Compressive Spectral Clustering

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

Large-Scale Image Segmentation with Convolutional Networks

Large-Scale Image Segmentation with Convolutional Networks

Safe Adaptive Importance Sampling

Shapes from Pixels

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Compressive Spectral Clustering

How to Boost Face Recognition with StyleGAN?

Just Sort It! A Simple and Effective Approach to Active Preference Learning