Search Results - "Chatelain, Clement"

DAN: A Segmentation-Free Document Attention Network for Handwritten Document Recognition by Coquenet, Denis, Chatelain, Clement, Paquet, Thierry

“…Unconstrained handwritten text recognition is a challenging computer vision task. It is traditionally handled by a two-step approach, combining line…”
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End-to-End Handwritten Paragraph Text Recognition Using a Vertical Attention Network by Coquenet, Denis, Chatelain, Clement, Paquet, Thierry

“…Unconstrained handwritten text recognition remains challenging for computer vision systems. Paragraph text recognition is traditionally achieved by two models:…”
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Dynamic Graph Representation Learning With Neural Networks: A Survey by Yang, Leshanshui, Chatelain, Clement, Adam, Sebastien

“…In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological…”
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Multi-Task Deep Learning for Pedestrian Detection, Action Recognition and Time to Cross Prediction by Pop, Danut Ovidiu, Rogozan, Alexandrina, Chatelain, Clement, Nashashibi, Fawzi, Bensrhair, Abdelaziz

“…A pedestrian detection system is a crucial component of advanced driver assistance systems since it contributes to road flow safety. The safety of traffic…”
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Pseudo-Labeling With Large Language Models for Multi-Label Emotion Classification of French Tweets by Malik, Usman, Bernard, Simon, Pauchet, Alexandre, Chatelain, Clément, Picot-Clémente, Romain, Cortinovis, Jérôme

“…This study proposes a novel semi-supervised multi-label emotion classification approach for French tweets based on pseudo-labeling. Human subjectivity in…”
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Nonlinear post-selection inference for genome-wide association studies by Slim, Lotfi, Chatelain, Clément, Azencott, Chloé-Agathe

“…To address the lack of statistical power and interpretability of genome-wide association studies (GWAS), gene-level analyses combine the p-values of individual…”
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Cascading BLSTM networks for handwritten word recognition by Stuner, Bruno, Chatelain, Clement, Paquet, Thierry

“…Handwritten word recognition is a tough task, mixing image and natural language processing. Recently new recurrent neural networks with LSTM cells allowed…”
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Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network by Coquenet, Denis, Chatelain, Clement, Paquet, Thierry

“…Unconstrained handwritten text recognition is a major step in most document analysis tasks. This is generally processed by deep recurrent neural networks and…”
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Handwriting recognition using cohort of LSTM and lexicon verification with extremely large lexicon by Stuner, Bruno, Chatelain, Clément, Paquet, Thierry

“…In this article, a handwriting recognition model whose complexity does not depend on the lexicon size is proposed. It is an alternative to lexicon-driven…”
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Gesture sequence recognition with one shot learned CRF/HMM hybrid model by Belgacem, Selma, Chatelain, Clément, Paquet, Thierry

“…In this paper, we propose a novel markovian hybrid system CRF/HMM for gesture recognition, and a novel motion description method called gesture signature for…”
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Fast object detection in compressed JPEG Images by Deguerre, Benjamin, Chatelain, Clement, Gasso, Gilles

“…Object detection in still images has drawn a lot of attention over past few years, and with the advent of Deep Learning impressive performances have been…”
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Fully convolutional network with dilated convolutions for handwritten text line segmentation by Renton, Guillaume, Soullard, Yann, Chatelain, Clément, Adam, Sébastien, Kermorvant, Christopher, Paquet, Thierry

“…We present a learning-based method for handwritten text line segmentation in document images. Our approach relies on a variant of deep fully convolutional…”
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Multi-scale Gated Fully Convolutional DenseNets for semantic labeling of historical newspaper images by Soullard, Yann, Tranouez, Pierrick, Chatelain, Clément, Nicolas, Stéphane, Paquet, Thierry

“…•A new fully convolutional network for historical newspaper semantic labeling.•A multi-scale gate block inspired by dense nets and gating mechanism is…”
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Novel methods for epistasis detection in genome-wide association studies by Slim, Lotfi, Chatelain, Clément, Azencott, Chloé-Agathe, Vert, Jean-Philippe

“…More and more genome-wide association studies are being designed to uncover the full genetic basis of common diseases. Nonetheless, the resulting loci are…”
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Performance of epistasis detection methods in semi-simulated GWAS by Chatelain, Clément, Durand, Guillermo, Thuillier, Vincent, Augé, Franck

“…Part of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called…”
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The Multiclass ROC Front method for cost-sensitive classification by Bernard, Simon, Chatelain, Clément, Adam, Sébastien, Sabourin, Robert

“…This paper addresses the problem of learning a multiclass classification system that can suit to any environment. By that we mean that particular (imbalanced)…”
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Spotting L3 slice in CT scans using deep convolutional network and transfer learning by Belharbi, Soufiane, Chatelain, Clément, Hérault, Romain, Adam, Sébastien, Thureau, Sébastien, Chastan, Mathieu, Modzelewski, Romain

“…Abstract In this article, we present a complete automated system for spotting a particular slice in a complete 3D Computed Tomography exam (CT scan). Our…”
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End-to-End Traffic Flow Rate Estimation From MPEG4 part-2 Compressed Video Streams by Deguerre, Benjamin, Chatelain, Clement, Gasso, Gilles

“…Automatic traffic surveillance usually relies on the estimation of traffic flow parameters through either dedicated sensors or the processing of road…”
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A systematic analysis of gene-gene interaction in multiple sclerosis by Slim, Lotfi, Chatelain, Clément, Foucauld, Hélène de, Azencott, Chloé-Agathe

“…For the most part, genome-wide association studies (GWAS) have only partially explained the heritability of complex diseases. One of their limitations is to…”
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Deep neural networks regularization for structured output prediction by Belharbi, Soufiane, Hérault, Romain, Chatelain, Clément, Adam, Sébastien

“…A deep neural network model is a powerful framework for learning representations. Usually, it is used to learn the relation x → y by exploiting the…”
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