UC San Diego

Statistical machine learning techniques have transformed computer vision research in the last two decades, and have led to many breakthroughs in object detection, recognition and tracking. Such data-driven methods extrapolate rules from a set of labeled examples, freeing us from designing and tuning a system by hand for a particular application or domain. Discriminative learning methods, which directly learn to differentiate categories of data rather than modeling the data itself, have been shown to be particularly effective. However, the requirement of a large set of labeled examples becomes prohibitively expensive, especially if we consider scaling to a wide range of domains and applications. In this dissertation we explore weakly supervised methods of training discriminative models for a number of computer vision applications. These methods require weaker forms of annotation that are easier and/or cheaper to obtain, and can learn in situations where the ground truth is inherently ambiguous. Many of the algorithms in this dissertation are based on a particular form of weakly supervised learning called Multiple Instance Learning (MIL). Our final contribution is a theoretical analysis of MIL that takes into account the characteristics of applications in computer vision and related areas