DeepVision Project

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 different viewpoints, shapes, etc. make learning an appealing approach. However, the current state of the art suffers 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.




Latest publications

Object Level Visual Reasoning in Videos

Fabien Baradel, Natalia Neverova, Christian Wolf, Julien Mille Greg Mori

IEEE European Conference on Computer Vision (ECCV) (2018)

Papier Project page Code Poster

author = {Baradel, Fabien and Neverova, Natalia and Wolf, Christian and Mille, Julien and Mori, Greg},
title = {Object Level Visual Reasoning in Videos},
booktitle = {ECCV},
month = {June},
year = {2018}

DualDis: Dual-Branch Disentangling with Adversarial Learning

Thomas Robert, Nicolas Thome, Matthieu Cord

Preprint. Under review at Advances in Neural Information Processing Systems (NIPS) (2019)


End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection

Taylor Mordan, Nicolas Thome, Gilles Henaff, Matthieu Cord

International Journal of Computer Vision (IJCV) (2018)


HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning

Thomas Robert, Nicolas Thome, Matthieu Cord

IEEE European Conference on Computer Vision (ECCV) (2018)


See all our publications