Extreme Classification

Extreme classification is a rapidly growing research area in computer vision focusing 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. To foster research in Extreme Classification, we have released datasets, codebases, benchmarks, and other useful resources at The Extreme Classification Repository (manikvarma.org) (opens in new tab).