C
Christian Wolf
Researcher at Institut national des sciences Appliquées de Lyon
Publications - 158
Citations - 5505
Christian Wolf is an academic researcher from Institut national des sciences Appliquées de Lyon. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 31, co-authored 148 publications receiving 4513 citations. Previous affiliations of Christian Wolf include Centre national de la recherche scientifique & University of Lyon.
Papers
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Book ChapterDOI
Sequential deep learning for human action recognition
TL;DR: A fully automated deep model, which learns to classify human actions without using any prior knowledge is proposed, which outperforms existing deep models, and gives comparable results with the best related works.
Journal ArticleDOI
Object count/area graphs for the evaluation of object detection and segmentation algorithms
TL;DR: The performance of a detection algorithm is illustrated intuitively by performance graphs which present object level precision and recall depending on constraints on detection quality, and a representative single performance value is computed from the graphs.
Journal ArticleDOI
ModDrop: Adaptive Multi-Modal Gesture Recognition
TL;DR: The proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities, and demonstrates the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.
Journal ArticleDOI
ICDAR 2003 robust reading competitions: entries, results, and future directions
Simon M. Lucas,Alex Panaretos,Luis Sosa,Anthony Tang,Shirley Wong,Robert Young,Kazuki Ashida,Hiroki Nagai,Masayuki Okamoto,Hiroaki Yamamoto,Hidetoshi Miyao,Junmin Zhu,Wuwen Ou,Christian Wolf,Jean-Michel Jolion,Leon Todoran,Marcel Worring,Xiaofan Lin +17 more
TL;DR: This paper broke down the robust reading problem into three subproblems and run competitions for each stage, and also a competition for the best overall system, and described an algorithm for combining the outputs of the individual text locators and showed how the combination scheme improves on any of theindividual systems.
Proceedings ArticleDOI
Text localization, enhancement and binarization in multimedia documents
TL;DR: An algorithm to localize artificial text in images and videos using a measure of accumulated gradients and morphological post processing to detect the text is presented and the quality of the localized text is improved by robust multiple frame integration.