Search Results for author: Clément Chatelain

Found 13 papers, 9 papers with code

Temporal receptive field in dynamic graph learning: A comprehensive analysis

1 code implementation17 Jul 2024 Yannis Karmim, Leshanshui Yang, Raphaël Fournier S'Niehotta, Clément Chatelain, Sébastien Adam, Nicolas Thome

Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges.

Benchmarking Dynamic Link Prediction +2

Dynamic Graph Representation Learning with Neural Networks: A Survey

no code implementations12 Apr 2023 Leshanshui Yang, Sébastien Adam, Clément Chatelain

In this research area, Dynamic Graph Neural Network (DGNN) has became the state of the art approach and plethora of models have been proposed in the very recent years.

Graph Learning Graph Neural Network +2

Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition

1 code implementation25 Jan 2023 Denis Coquenet, Clément Chatelain, Thierry Paquet

Recent advances in handwritten text recognition enabled to recognize whole documents in an end-to-end way: the Document Attention Network (DAN) recognizes the characters one after the other through an attention-based prediction process until reaching the end of the document.

Handwritten Document Recognition

DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition

1 code implementation23 Mar 2022 Denis Coquenet, Clément Chatelain, Thierry Paquet

For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network.

Decoder Handwritten Document Recognition +1

Have convolutions already made recurrence obsolete for unconstrained handwritten text recognition ?

no code implementations9 Dec 2020 Denis Coquenet, Yann Soullard, Clément Chatelain, Thierry Paquet

This has a direct influence on the training time of such architectures, with also a direct consequence on the time required to explore various architectures.

Handwriting Recognition Handwritten Text Recognition

End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network

1 code implementation7 Dec 2020 Denis Coquenet, Clément Chatelain, Thierry Paquet

For each text line features, a decoder module recognizes the character sequence associated, leading to the recognition of a whole paragraph.

Decoder Handwritten Text Recognition

Fast object detection in compressed JPEG Images

2 code implementations16 Apr 2019 Benjamin Deguerre, Clément Chatelain, Gilles Gasso

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 achieved with numerous industrial applications.

Object object-detection +1

LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction

no code implementations24 Jul 2017 Bruno Stuner, Clément Chatelain, Thierry Paquet

Offline handwritten text line recognition is a hard task that requires both an efficient optical character recognizer and language model.

Handwriting Recognition Language Modelling

Handwriting recognition using Cohort of LSTM and lexicon verification with extremely large lexicon

no code implementations22 Dec 2016 Bruno Stuner, Clément Chatelain, Thierry Paquet

State-of-the-art methods for handwriting recognition are based on Long Short Term Memory (LSTM) recurrent neural networks (RNN), which now provides very impressive character recognition performance.

Handwriting Recognition

Deep Neural Networks Regularization for Structured Output Prediction

1 code implementation28 Apr 2015 Soufiane Belharbi, Romain Hérault, Clément Chatelain, Sébastien Adam

The motivation of this work is to learn the output dependencies that may lie in the output data in order to improve the prediction accuracy.

Facial Landmark Detection

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