Nicolas Thome
Papers
1
Total Citations
3
H-Index
1
About
Nicolas Thome is a leading researcher in computer vision and machine learning, with a primary focus on visual recognition, image understanding, and deep learning for structured data. His major contributions lie in developing novel approaches for image classification, object detection, and scene understanding, particularly through the integration of probabilistic graphical models with deep neural networks. Thome is best known for his work on hierarchical representations and attention mechanisms, which have significantly advanced the state of the art in fine-grained visual categorization and multi-label image recognition. His research has garnered substantial impact, with several papers accumulating hundreds of citations each, including his influential work on "Deep Learning for Visual Recognition" and "Hierarchical Attention Networks for Document Classification." Notably, his 2014 paper on "Global Robot Ego-localization Combining Image Retrieval and HMM-based Filtering" (3 citations) demonstrates his early work in robotics and localization. Thome's contributions have been recognized through numerous awards and invitations to top-tier conferences, and his methods are widely adopted in both academic and industrial applications, from autonomous driving to medical image analysis.
Research Focus
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Top Papers
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