WILLOW is based in the Laboratoire d'Informatique de l'École Normale Superiéure (CNRS/ENS/INRIA UMR 8548) and is a joint research team between
École Normale Supérieure
de Paris and
Centre National de la Recherche Scientifique.
Our research is concerned with representational issues in visual object recognition and scene understanding. Our objective is to develop geometric, physical, and statistical models for all components of the image interpretation process, including illumination, materials, objects, scenes, and human activities. These models will be used to tackle fundamental scientific challenges such as three-dimensional (3D) object and scene modeling, analysis, and retrieval; human activity capture and classification; and category-level object and scene recognition. They will also support applications with high scientific, societal, and/or economic impact in domains such as quantitative image analysis in domains such as archaeology and cultural heritage conservation; film post-production and special effects; and video annotation, interpretation, and retrieval. Moreover, machine learning now represents a significant part of computer vision research, and one of the aims of the project is to foster the joint development of contributions to machine learning and computer vision, together with algorithmic and theoretical work on generic statistical machine learning.
We follow four main research directions: