Coming soon ...
Call for participantsExtreme classification is a rapidly growing research area in computer vision focussing on multi-class and multi-label problems involving an extremely large number of labels (ranging from thousands to billions). Many applications of extreme classification have been found in diverse areas including recognizing faces, retail products and landmarks; image and video tagging; etc. Extreme classification reformulations have led to significant gains over traditional ranking and recommendation techniques for both machine learning and computer vision applications leading to their deployment in several popular products used by millions of people worldwide. This has come about due to recent key advances in modeling structural relations among labels, the development of sub-linear time algorithms for training and inference, the development of appropriate loss-functions which are unbiased with respect to missing labels and provide greater rewards for the accurate prediction of rare labels, etc.
Extreme classification raises a number of interesting research questions including but not limited to:
The workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. Several leading researchers will present invited talks detailing the latest advances in the area. The workshop should be of interest to researchers in core supervised learning as well as application domains such as visual recognition, search, and recommender systems. We expect healthy participation from both industry and academia.
LinksExtreme classification resources
The 2018 Dagstuhl Seminar on Extreme Classification