Modeling the Importance of Faces in Natural Images

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Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

The choice of the shape parameter highly effects the behaviour of radial basis function (RBF) approximations, as it needs to be selected to balance between the ill-conditioning of the interpolation matrix and high accuracy. In this paper, we demonstrate ho ...

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

The current leading 6D object pose estimation methods rely heavily on annotated real data, which is highly costly to acquire. To overcome this, many works have proposed to introduce computer-generated synthetic data. However, bridging the gap between the s ...

The benefits and limitations inherent to the 2D post-processing of measurements from Brillouin optical time-domain analyzers are investigated from a fundamental point of view. In a preliminary step, the impact of curve fitting on the precision of the estim ...

Random binning features, introduced in the seminal paper of Rahimi and Recht '07, are an efficient method for approximating a kernel matrix using locality sensitive hashing. Random binning features provide a very simple and efficient way to approximate the ...

We propose a deep neural network based image-to-image translation for domain adaptation, which aims at finding translations between image domains. Despite recent GAN based methods showing promising results in image-to-image translation, they are prone to f ...

Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimensional spaces. A major disadvantage of kernel methods is their poor scalabili ...

This thesis focuses on developing efficient algorithmic tools for processing large datasets. In many modern data analysis tasks, the sheer volume of available datasets far outstrips our abilities to process them. This scenario commonly arises in tasks incl ...

In this paper we revisit the kernel density estimation problem: given a kernel K(x, y) and a dataset of n points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query q, a (1 + epsilon)-approximation to mu := ...

Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics. We achieve this by replac ...

Attribute guided face image synthesis aims to manipulate attributes on a face image. Most existing methods for image-to-image translation can either perform a fixed translation between any two image domains using a single attribute or require training data ...

Face recognition is a mature field in biometrics in which several systems have been proposed over the last three decades. Such systems are extremely reliable under controlled recording conditions and it has been deployed in the field in critical tasks, suc ...

Cross-domain synthesizing realistic faces to learn deep models has attracted increasing attention for facial expression analysis as it helps to improve the performance of expression recognition accuracy despite having small number of real training images. ...

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...

We report measurements of the production cross sections of charged pions, kaons, and protons as a function of fractional energy, the event-shape variable called thrust, and the transverse momentum with respect to the thrust axis. These measurements access ...

The stochastic gravitational wave background (SGWB) contains a wealth of information on astrophysical and cosmological processes. A major challenge of upcoming years will be to extract the information contained in this background and to disentangle the con ...

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 ...

This research studies the performance of eddy and non-eddy viscosity subgrid-scale (SGS) models using direct numerical simulations (DNS) of stratified turbulence. Nonlinear and modulated gradient models, as the two common non-eddy viscosity SGS parameteriz ...

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging method ...

Modeling the Importance of Faces in Natural Images

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Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

A new variable shape parameter strategy for RBF approximation using neural networks

Improving the Training of Compact Neural Networks for Visual Recognition

SD-Pose: Semantic Decomposition for Cross-Domain 6D Object Pose Estimation

On the 2D Post-Processing of Brillouin Optical Time-Domain Analysis

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

ExprADA: Adversarial domain adaptation for facial expression analysis

Oblivious Sketching of High-Degree Polynomial Kernels

Fourier Sampling in Signal Processing and Numerical Linear Algebra

Kernel Density Estimation through Density Constrained Near Neighbor Search

Pairwise Comparisons with Flexible Time-Dynamics

Learn to synthesize and synthesize to learn

Learning How To Recognize Faces In Heterogeneous Environments

Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Transverse momentum dependent production cross sections of charged pions, kaons and protons produced in inclusive e(+)e(-) annihilation at root s=10.58 GeV

Probing non-Gaussian stochastic gravitational wave backgrounds with LISA

Large-Scale Image Segmentation with Convolutional Networks

Evaluation of non-eddy viscosity subgrid-scale models in stratified turbulence using direct numerical simulations

A Fourier dimensionality reduction model for big data interferometric imaging