World's Best Scientists 2026 revealed!
Nicolas Thome

Nicolas Thome

D-Index & Metrics

Computer Science

D-Index
35
Citations
4993
World Ranking
11734
National Ranking
292

Overview

Nicolas Thome is affiliated with Sorbonne University in France. Their research contributions lie primarily within the field of Computer Science, with a focus on several related subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Signal Processing, and Statistical and Nonlinear Physics.

Their work encompasses a range of contemporary topics such as Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, Advanced Image and Video Retrieval Techniques, Anomaly Detection Techniques and Applications, Advanced Neural Network Applications, Image Retrieval and Classification Techniques, and Generative Adversarial Networks and Image Synthesis.

Thome has published extensively in well-known venues. These frequent publication channels include:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Computer Vision and Image Understanding
  • 2022 26th International Conference on Pattern Recognition (ICPR)

Collaborations feature prominently in their research profile, with frequent co-authors including Clément Rambour, Matthieu Cord, Elias Ramzi, Nicolas Audebert, and Toby Collins, reflecting a network of partnerships in related research areas.

Recent papers authored or co-authored by Nicolas Thome include:

  • "Augmenting physical models with deep networks for complex dynamics forecasting" (2021), published in Journal of Statistical Mechanics Theory and Experiment
  • "Deep Time Series Forecasting With Shape and Temporal Criteria" (2022), published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction" (2020), published on arXiv (Cornell University)
  • "Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity" (2020), published on arXiv (Cornell University)
  • "U-Net Transformer: Self and Cross Attention for Medical Image Segmentation" (2021), published on arXiv (Cornell University)

Best Publications

  • MUTAN: Multimodal Tucker Fusion for Visual Question Answering

    Hedi Ben-younes;Remi Cadene;Matthieu Cord;Nicolas Thome

  • WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation

    Thibaut Durand;Taylor Mordan;Nicolas Thome;Matthieu Cord

  • MUREL: Multimodal Relational Reasoning for Visual Question Answering

    Remi Cadene;Hedi Ben-younes;Matthieu Cord;Nicolas Thome

  • Pooling in image representation: The visual codeword point of view

    Sandra Avila;Nicolas Thome;Matthieu Cord;Eduardo Valle

  • Addressing Failure Prediction by Learning Model Confidence

    Charles Corbière;Nicolas Thome;Avner Bar-Hen;Matthieu Cord

  • BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection

    Hedi Ben-younes;Remi Cadene;Nicolas Thome;Matthieu Cord

  • Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings

    Micael Carvalho;Rémi Cadène;David Picard;Laure Soulier

  • Recipe recognition with large multimodal food dataset

    Xin Wang;Devinder Kumar;Nicolas Thome;Matthieu Cord

  • WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks

    Thibaut Durand;Nicolas Thome;Matthieu Cord

  • Disentangling Physical Dynamics From Unknown Factors for Unsupervised Video Prediction

    Vincent Le Guen;Nicolas Thome

  • U-Net Transformer: Self and Cross Attention for Medical Image Segmentation

    Olivier Petit;Nicolas Thome;Clement Rambour;Loic Themyr

  • A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach

    N. Thome;S. Miguet;S. Ambellouis

  • Learning Deep Hierarchical Visual Feature Coding

    Hanlin Goh;Nicolas Thome;Matthieu Cord;Joo-Hwee Lim

  • T-HOG: An effective gradient-based descriptor for single line text regions

    Rodrigo Minetto;Nicolas Thome;Matthieu Cord;Neucimar J. Leite

  • Quadruplet-Wise Image Similarity Learning

    Marc T. Law;Nicolas Thome;Matthieu Cord

  • Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models

    Vincent Le Guen;Nicolas Thome

  • BOSSA: Extended bow formalism for image classification

    S. Avila;N. Thome;M. Cord;E. Valle

  • Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis

    Christian Theriault;Nicolas Thome;Matthieu Cord

  • Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

    Vincent Le Guen;Yuan Yin;Jérémie Dona;Ibrahim Ayed

  • Extended Coding and Pooling in the HMAX Model

    C. Theriault;N. Thome;M. Cord

  • A cognitive and video-based approach for multinational License Plate Recognition

    Nicolas Thome;Antoine Vacavant;Lionel Robinault;Serge Miguet

  • Addressing Failure Prediction by Learning Model Confidence

    Charles Corbière;Nicolas Thome;Avner Bar-Hen;Matthieu Cord

  • Gossip training for deep learning

    Michael Blot;David Picard;Matthieu Cord;Nicolas Thome

  • SnooperText: A Text Detection System for Automatic Indexing of Urban Scenes

    Rodrigo Minetto;Nicolas Thome;Matthieu Cord;Neucimar J. Leite

  • Snoopertext: A multiresolution system for text detection in complex visual scenes

    R. Minetto;N. Thome;M. Cord;J. Fabrizio

Frequent Co-Authors

Matthieu Cord
Matthieu Cord Sorbonne University
Luc Soler
Luc Soler University of Strasbourg
Patrick Gallinari
Patrick Gallinari Sorbonne University
Joo-Hwee Lim
Joo-Hwee Lim Agency for Science, Technology and Research
Ludovic Denoyer
Ludovic Denoyer Sorbonne University
Alain Rakotomamonjy
Alain Rakotomamonjy Criteo (France)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education in computer science opens up several flexible paths and career opportunities. Students seeking accessible options may consider 2 year online degrees, which allow for a quicker entry into the tech workforce. For those aiming to advance their credentials, pursuing masters degrees that are worth it can lead to specialized roles and higher earning potential.

Cost and admission requirements are often major concerns. Thankfully, there are cheap online college classes that make earning a degree feasible without incurring heavy student debt. Additionally, students with a less competitive GPA can still pursue their ambitions thanks to a range of online colleges that accept low gpa.

Whether you’re seeking an associate’s, bachelor’s, or master’s degree, affordable and accessible online programs can help you launch or boost your tech career, no matter where you start.

Best Scientists Citing Nicolas Thome

Trending Scientists

Recently Published Articles