Building the howto100m Video Corpus

<p>Video annotation is an expensive and time-consuming process. As a consequence, the available video datasets are useful but small. The availability of machine transcribed explainer videos offers a unique opportunity to rapidly develop a useful, if dirty, corpus of videos that are "self annotating", as hosts explain the actions they are taking on the screen.</p> <p>This episode is a discussion of the <a href= "https://www.di.ens.fr/willow/research/howto100m/">HowTo100m</a> dataset - a project which has assembled a video corpus of 136M video clips with captions covering 23k activities.</p> <h3>Related Links</h3> <p>The paper will be presented at <a href= "http://iccv2019.thecvf.com/">ICCV 2019</a></p> <p><a href="https://twitter.com/antoine77340">@antoine77340</a></p> <p><a href="https://github.com/antoine77340">Antoine on Github</a></p> <p><a href="https://www.di.ens.fr/~miech/">Antoine's homepage</a></p>

Guest

Antoine Miech: "Antoine Miech is a third 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. His main research interests are video understanding and weakly-supervised machine learning. More generally,He is interested in everything related to Computer Vision, Machine Learning and Natural Language Processing. During the 2018 summer, He had the chance to collaborate with Du Tran, Heng Wang and Lorenzo Torresani at Facebook AI. He was also awarded the Google Ph.D. fellowship in 2018. "