DeepVision is an international research project funded by French Agency ANR and Canadian Agency NSERC. Ongoing (2016-2020), it proposes fundamental and applied research in the areas Machine Learning and Computer Vision. In particular:

Advances in computer vision have enabled applications that seemed impossible only a few years ago, e.g. gesture recognition in real time, face detection on mobile devices and 3d mapping of environments. Machine learning is a major driving force behind this development. The vast amount of data corresponding to visual information, as well as its inherently large variability due to di↵erent viewpoints, shapes, etc. make learning an appealing approach. However, the current state of the art su↵ers from limitations. Structural relationships put many realistic situations out of reach of commercial applications, such as person-person and person-object interactions, long-term dynamical behavior, and the recognition of highly deformable and articulated objects. We propose to address these challenges by creating deep structured models, combining the advantages of deep learning, a powerful family of models and algorithms, with structured models, which are well-suited for modeling complex data.

• University of Guelph ( Graham W. Taylor)
• Simon Fraser University ( Greg Mori)
• INSA-Lyon / LIRIS ( Christian Wolf , Julien Mille; Fabien Baradel )
• UPMC / LIP6 ( Matthieu Cord , Nicolas Thome )

• Publication list of the project
• Planning du projet