Search Results for author: Fernando Pereira

Found 15 papers, 3 papers with code

Joint Geometry and Color Projection-based Point Cloud Quality Metric

1 code implementation5 Aug 2021 Alireza Javaheri, Catarina Brites, Fernando Pereira, João Ascenso

Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images.

Point Cloud Quality Assessment

Multi-Perspective LSTM for Joint Visual Representation Learning

1 code implementation CVPR 2021 Alireza Sepas-Moghaddam, Fernando Pereira, Paulo Lobato Correia, Ali Etemad

We validate the performance of our proposed architecture in the context of two multi-perspective visual recognition tasks namely lip reading and face recognition.

Face Recognition Lip Reading +1

CapsField: Light Field-based Face and Expression Recognition in the Wild using Capsule Routing

no code implementations10 Jan 2021 Alireza Sepas-Moghaddam, Ali Etemad, Fernando Pereira, Paulo Lobato Correia

A subset of the in the wild dataset contains facial images with different expressions, annotated for usage in the context of face expression recognition tests.

Faithful Embeddings for Knowledge Base Queries

1 code implementation NeurIPS 2020 Haitian Sun, Andrew O. Arnold, Tania Bedrax-Weiss, Fernando Pereira, William W. Cohen

We address this problem with a novel QE method that is more faithful to deductive reasoning, and show that this leads to better performance on complex queries to incomplete KBs.

Question Answering

Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition

no code implementations11 May 2019 Alireza Sepas-Moghaddam, Ali Etemad, Fernando Pereira, Paulo Lobato Correia

In this context, this paper proposes two novel LSTM cell architectures that are able to jointly learn from multiple sequences simultaneously acquired, targeting to create richer and more effective models for recognition tasks.

Face Recognition Time Series

Face Recognition: A Novel Multi-Level Taxonomy based Survey

no code implementations3 Jan 2019 Alireza Sepas-Moghaddam, Fernando Pereira, Paulo Lobato Correia

In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment.

Face Recognition

A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition

no code implementations25 May 2018 Alireza Sepas-Moghaddam, Mohammad A. Haque, Paulo Lobato Correia, Kamal Nasrollahi, Thomas B. Moeslund, Fernando Pereira

This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task.

Face Recognition

Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix Language Model

no code implementations5 Nov 2015 Ciprian Chelba, Fernando Pereira

In experiments on the one billion words language modeling benchmark, we are able to slightly improve on our previous results which use a different loss function, and employ leave-one-out training on a subset of the main training set.

Language Modelling

Controlling Complexity in Part-of-Speech Induction

no code implementations16 Jan 2014 João V. Graça, Kuzman Ganchev, Luisa Coheur, Fernando Pereira, Ben Taskar

We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text.

Posterior vs Parameter Sparsity in Latent Variable Models

no code implementations NeurIPS 2009 Kuzman Ganchev, Ben Taskar, Fernando Pereira, João Gama

We apply this new method to learn first-order HMMs for unsupervised part-of-speech (POS) tagging, and show that HMMs learned this way consistently and significantly out-performs both EM-trained HMMs, and HMMs with a sparsity-inducing Dirichlet prior trained by variational EM.

Latent Variable Models Part-Of-Speech Tagging +1

Exact Convex Confidence-Weighted Learning

no code implementations NeurIPS 2008 Koby Crammer, Mark Dredze, Fernando Pereira

Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that represents uncertainty about weights and correlations.

Learning Bounds for Domain Adaptation

no code implementations NeurIPS 2007 John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman

Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain.

Domain Adaptation

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