I am a second year Computer Vision and Machine Learning Ph.D. student in the WILLOW project-team which is part of Inria and Ecole Normale Supérieure, 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.
Video Dataset Overview is a Searchable and sortable compilation of annotated video datasets I am currently maintaining. It is supposed to help people to have a global overview of the existing annotated video datasets as well as some important features such as their size, published year or annotation type.
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.