Section: Overall Objectives

Statement

Object recognition —or, in a broader sense, scene understanding— is the ultimate scientific challenge of computer vision: After 40 years of research, robustly identifying the familiar objects (chair, person, pet), scene categories (beach, forest, office), and activity patterns (conversation, dance, picnic) depicted in family pictures, news segments, or feature films is still far beyond the capabilities of today's vision systems. On the other hand, truly successful object recognition and scene understanding technology will have a broad impact in application domains as varied as defense, entertainment, health care, human-computer interaction, image retrieval and data mining, industrial and personal robotics, manufacturing, scientific image analysis, surveillance and security, and transportation.

Despite the limitations of today's scene understanding technology, tremendous progress has been accomplished in the past ten years, due in part to the formulation of object recognition as a statistical pattern matching problem. The emphasis is in general on the features defining the patterns and on the algorithms used to learn and recognize them, rather than on the representation of object, scene, and activity categories, or the integrated interpretation of the various scene elements. WILLOW complements this approach with an ambitious research program explicitly addressing the representational issues involved in object recognition and, more generally, scene understanding.

Concretely, 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 science and humanities; film post-production and special effects; and video annotation, interpretation, and retrieval. Machine learning is a key part of our effort, with a balance of practical work in support of computer vision application and methodological research aimed at developing effective algorithms and architectures.

WILLOW was created in 2007: It was recognized as an Inria team in January 2007, and as an official project-team in June 2007. WILLOW is a joint research team between Inria Paris Rocquencourt, Ecole Normale Supérieure (ENS) and Centre National de la Recherche Scientifique (CNRS).

This year we have hired two new Phd students: Piotr Bojanowski (Inria) and Vadim Kantorov (Inria). Minsu Cho, Visesh Chari and Jian Sun have been hired as post-docs. Alexei Efros (Professor, Carnegie Mellon University, USA) and René Vidal, (Associate Professor, Johns Hopkins University, USA) visited WILLOW in summer 2012. Five Willow PhD students have defended PhD in 2012: Y-Lan Boureau (currently postdoc at NYU), Olivier Duchenne (currently postdoc at CMU), Armand Joulin (currently postodc at Stanford Univ.), Muhammad Muneeb Ullah (currently faculty member at NUST-SEECS) and Oliver Whyte (currently at Microsoft).