WILLOW
Computer vision and machine learning research laboratory
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
INRIA Rocquencourt,
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.
ENS/INRIA Summer School, Paris, July 22-26, 2013
Research themes
We follow four main research directions:
- 3D object and scene modeling, analysis, and retrieval:
This part of our research addresses the problem of acquiring high-accuracy geometric models of complex 3D objects from multiple images, and using these models in rigid and nonrigid registration and retrieval tasks. - Human activity capture and classification:
This includes combining object detection and tracking to associate multiple ocurrences of faces and bodies in video footage, and constructing and recognizing spatio-temporal models of human activities and interactions. - Category-level object and scene recognition:
This part of our research is concerned with developing geometric and statistical models of objects, scenes, and their components that support semi-supervised learning, effectively handle image variability, and efficiently support inference. - Machine learning:
The team focuses on both algorithmic aspects related to vision applications and theoretical aspects: kernel methods, sparse methods, calibration methods, graphical models and multi-task learning.
