I am a first year Computer Vision and Machine Learning Ph.D. student in the WILLOW project-team which is part of Inria and Ecole Normale Supérieure de Paris, working with Ivan Laptev and Josef Sivic. My main research interests are Video understanding and weakly-supervised machine learning. More generally, I am interested in everything related to Computer Vision, Machine Learning and Natural Language Processing.
Abtsract: Discriminative clustering has been successfully applied to a number of weakly-supervised learning tasks. One drawback of discriminative clustering, however, is its limited scalability. We address this issue and propose an online optimization algorithm based on the Block-Coordinate Frank-Wolfe algorithm. We apply it to the problem of weakly-supervised learning of actions and actors from movies and corresponding movie scripts as supervision.
Abtract: We present state-of-the-art end-to-end learnable pooling method for video classification. Our method was used to achieve the best performance in the kaggle Youtube 8M challenge out of 650 teams.
LOUPE (Learnable mOdUle for Pooling fEatures) is a Tensorflow toolbox that implements several modules for pooling features such as NetVLAD, NetRVLAD, NetFV and Soft-DBoW. It also allows to use their Gated version. This toolbox was mainly use in the winning approach of the Youtube 8M Large Scale Video Understanding challenge.
The Data Science Game is a student only and worldwide machine learning competition. I have been involved in the project since 2016 as an organizer.The 2016 edition was very successful, we got invited at NIPS 2016 CiML Workshop to present this poster.