The See.4C challenge is a “warm up challenge” whose objective is to test the protocol and platform of a large-scale upcoming EU challenge to predict electricity flows in the French power network (2 million Euros in prizes).
We propose a different task, but of real practical interest: predicting upcoming frames in video data. The applications may include replacing missing frames in a tele-conference when data transmission is defective. For this benchmark, we are making available a dataset of thousands of videos of speakers facing a camera, sampled at 25 frames per second. We limit the resolution to small 32×32 pixels frames in black and white to permit obtaining results in a short time, in the context of a hackathon.
The problem is far from trivial. The baseline method consisting of “freezing” the frame and making constant prediction (persitence) is very difficult to beat. While computer vision methods may help, it will be interesting to see whether generic methods applicable to other domains can significantly outperform the baseline, including deep learning methods. Classical signal processing methods such as ARIMA models should also be serious contenders.
One day hackathon:
The launching event will be held at La Paillasse, Paris. This event is co-sponsored by the Paris Machine Learning Meetups. The challenge will remain open until April 2, 2017.
Florin Popescu (Fraunhofer Institute, Berlin, Germany)
Sergio Escalera, Xavier Baro, and Julio Jacques Jr. (University of Barcelona, Spain)
Cecile Capponi, Stephane Ayache, and Isabelle Guyon (Aix Marseille University)
Commitee and local arrangements:
Isabelle Guyon, Lisheng Sun, and Diviyan Kalainathan (UPsud Paris-Saclay and ChaLearn)
Igor Carron and Frank Bardol (Paris Machine-Learning Meetups)
Sebastien Treguer (La Paillasse and ChaLearn)
Balazs Kegl (CNRS, Paris-Saclay Center for Data Science)